This summary provides an overview of an academic paper that evaluates the performance of efficient transformer-based language models on an automated essay scoring dataset:
The paper explores using smaller, more efficient transformer models rather than larger ones like BERT for automated essay scoring (AES). It evaluates several efficient models - Albert, Reformer, Electra, and MobileBERT - on the ASAP AES dataset. By ensembling multiple efficient models, the paper achieves state-of-the-art results on the dataset using far fewer parameters than typical transformer models, challenging the idea that bigger models are always better for AES. The efficient models show potential for extending the maximum text length they can analyze and reducing computational requirements for AES.
Semantic Mask for Transformer Based End-to-End Speech RecognitionWhenty Ariyanti
The attention-based encoder-decoder model has achieved impressive results for both automatic speech recognition (ASR) and text-to-speech (TTS) tasks. Inspired by SpecAugment and BERT, this study proposed a semantic mask based regularization for training such kind of end-to-end (E2E) model. While this approach is applicable to the encoder-decoder framework with any type of Neural Network architecture, then study the transformer-based model for ASR and perform experiments on LibriSpeech 960h and TedLium2 dataset and achieve state-of-the-art performance on the test set in the scope of E2E models.
An Optimized Parallel Algorithm for Longest Common Subsequence Using Openmp –...IRJET Journal
This document summarizes research on developing parallel algorithms to optimize solving the longest common subsequence (LCS) problem. LCS is commonly used for sequence comparison in bioinformatics. Traditional sequential dynamic programming algorithms have complexity of O(mn) for sequences of lengths m and n. The document reviews parallel algorithms developed using tools like OpenMP and GPUs like CUDA to reduce computation time. It proposes the authors' own optimized parallel algorithm for multi-core CPUs using OpenMP.
The document proposes an automatic Arabic grammar error correction model based on expectation-maximization routing and bidirectional agreement between left-to-right and right-to-left models. It generates synthetic training data using back-translation and direct error injection. The model uses capsule networks and dynamic layer aggregation to efficiently combine information across layers. It also introduces a regularization term to minimize divergence between the two models and resolve the exposure bias problem during training. Experiments on two benchmarks show the proposed model achieves state-of-the-art performance for Arabic grammar error correction.
A Parallel Packed Memory Array to Store Dynamic GraphsSubhajit Sahu
Highlighted notes on A Parallel Packed Memory Array to Store Dynamic Graphs.
While doing research work under Prof. Kishore Kothapalli.
Here they provide proofs of a concurrently updateable data structure for dynamic graph (read this in FPGA paper).
It is an extension of CSR with free spaces in between so new edges can be added or removed. If there is no free space, edges can be redistributed within a tree-like hierarchy of blocks (leaves). If still no space, the size can be doubled. Looking up an edge still takes O(logN) like in CSR. They compare it to Aspen (dynamic graph streaming) and Ligra (static CSR) /Ligra+ (static compressed CSR) and find it to be close in performance to Ligra+ (Ligra slightly faster). Since PPCSR is not compressed and has free space, it occupies the most space. They use a (popcount based) lock order so that it is deadlock free.
11.query optimization to improve performance of the code executionAlexander Decker
1. The document discusses query optimization techniques to improve the performance of object querying in Java.
2. It presents the Java Query Language (JQL) which allows programmers to express queries over object collections in Java through a declarative syntax.
3. The key aspects of JQL implementation include a compiler that compiles JQL queries to Java code and a query evaluator that applies optimizations like hash joins and nested loops joins to efficiently evaluate the queries.
Query optimization to improve performance of the code executionAlexander Decker
This document discusses query optimization techniques to improve the performance of code execution. It describes how object querying provides an abstraction for operations over collections of objects that allows the query evaluator to optimize queries dynamically at runtime. Specifically, it presents an example of using the Java Query Language (JQL) to perform an equi-join on two collections in a more succinct way compared to manually iterating over the collections, and discusses how the JQL query could be optimized using techniques like hash joins.
Accelerating sparse matrix-vector multiplication in iterative methods using GPUSubhajit Sahu
Kiran Kumar Matam; Kishore Kothapalli
All Authors
19
Paper
Citations
1
Patent
Citation
486
Full
Text Views
Multiplying a sparse matrix with a vector (spmv for short) is a fundamental operation in many linear algebra kernels. Having an efficient spmv kernel on modern architectures such as the GPUs is therefore of principal interest. The computational challenges that spmv poses are significantlydifferent compared to that of the dense linear algebra kernels. Recent work in this direction has focused on designing data structures to represent sparse matrices so as to improve theefficiency of spmv kernels. However, as the nature of sparseness differs across sparse matrices, there is no clear answer as to which data structure to use given a sparse matrix. In this work, we address this problem by devising techniques to understand the nature of the sparse matrix and then choose appropriate data structures accordingly. By using our technique, we are able to improve the performance of the spmv kernel on an Nvidia Tesla GPU (C1060) by a factor of up to80% in some instances, and about 25% on average compared to the best results of Bell and Garland [3] on the standard dataset (cf. Williams et al. SC'07) used in recent literature. We also use our spmv in the conjugate gradient method and show an average 20% improvement compared to using HYB spmv of [3], on the dataset obtained from the The University of Florida Sparse Matrix Collection [9].
Published in: 2011 International Conference on Parallel Processing
Date of Conference: 13-16 Sept. 2011
Date Added to IEEE Xplore: 17 October 2011
ISBN Information:
ISSN Information:
INSPEC Accession Number: 12316254
DOI: 10.1109/ICPP.2011.82
Publisher: IEEE
Conference Location: Taipei City, Taiwan
Concurrent Matrix Multiplication on Multi-core ProcessorsCSCJournals
With the advent of multi-cores every processor has built-in parallel computational power and that can only be fully utilized only if the program in execution is written accordingly. This study is a part of an on-going research for designing of a new parallel programming model for multi-core architectures. In this paper we have presented a simple, highly efficient and scalable implementation of a common matrix multiplication algorithm using a newly developed parallel programming model SPC3 PM for general purpose multi-core processors. From our study it is found that matrix multiplication done concurrently on multi-cores using SPC3 PM requires much less execution time than that required using the present standard parallel programming environments like OpenMP. Our approach also shows scalability, better and uniform speedup and better utilization of available cores than that the algorithm written using standard OpenMP or similar parallel programming tools. We have tested our approach for up to 24 cores with different matrices size varying from 100 x 100 to 10000 x 10000 elements. And for all these tests our proposed approach has shown much improved performance and scalability
Semantic Mask for Transformer Based End-to-End Speech RecognitionWhenty Ariyanti
The attention-based encoder-decoder model has achieved impressive results for both automatic speech recognition (ASR) and text-to-speech (TTS) tasks. Inspired by SpecAugment and BERT, this study proposed a semantic mask based regularization for training such kind of end-to-end (E2E) model. While this approach is applicable to the encoder-decoder framework with any type of Neural Network architecture, then study the transformer-based model for ASR and perform experiments on LibriSpeech 960h and TedLium2 dataset and achieve state-of-the-art performance on the test set in the scope of E2E models.
An Optimized Parallel Algorithm for Longest Common Subsequence Using Openmp –...IRJET Journal
This document summarizes research on developing parallel algorithms to optimize solving the longest common subsequence (LCS) problem. LCS is commonly used for sequence comparison in bioinformatics. Traditional sequential dynamic programming algorithms have complexity of O(mn) for sequences of lengths m and n. The document reviews parallel algorithms developed using tools like OpenMP and GPUs like CUDA to reduce computation time. It proposes the authors' own optimized parallel algorithm for multi-core CPUs using OpenMP.
The document proposes an automatic Arabic grammar error correction model based on expectation-maximization routing and bidirectional agreement between left-to-right and right-to-left models. It generates synthetic training data using back-translation and direct error injection. The model uses capsule networks and dynamic layer aggregation to efficiently combine information across layers. It also introduces a regularization term to minimize divergence between the two models and resolve the exposure bias problem during training. Experiments on two benchmarks show the proposed model achieves state-of-the-art performance for Arabic grammar error correction.
A Parallel Packed Memory Array to Store Dynamic GraphsSubhajit Sahu
Highlighted notes on A Parallel Packed Memory Array to Store Dynamic Graphs.
While doing research work under Prof. Kishore Kothapalli.
Here they provide proofs of a concurrently updateable data structure for dynamic graph (read this in FPGA paper).
It is an extension of CSR with free spaces in between so new edges can be added or removed. If there is no free space, edges can be redistributed within a tree-like hierarchy of blocks (leaves). If still no space, the size can be doubled. Looking up an edge still takes O(logN) like in CSR. They compare it to Aspen (dynamic graph streaming) and Ligra (static CSR) /Ligra+ (static compressed CSR) and find it to be close in performance to Ligra+ (Ligra slightly faster). Since PPCSR is not compressed and has free space, it occupies the most space. They use a (popcount based) lock order so that it is deadlock free.
11.query optimization to improve performance of the code executionAlexander Decker
1. The document discusses query optimization techniques to improve the performance of object querying in Java.
2. It presents the Java Query Language (JQL) which allows programmers to express queries over object collections in Java through a declarative syntax.
3. The key aspects of JQL implementation include a compiler that compiles JQL queries to Java code and a query evaluator that applies optimizations like hash joins and nested loops joins to efficiently evaluate the queries.
Query optimization to improve performance of the code executionAlexander Decker
This document discusses query optimization techniques to improve the performance of code execution. It describes how object querying provides an abstraction for operations over collections of objects that allows the query evaluator to optimize queries dynamically at runtime. Specifically, it presents an example of using the Java Query Language (JQL) to perform an equi-join on two collections in a more succinct way compared to manually iterating over the collections, and discusses how the JQL query could be optimized using techniques like hash joins.
Accelerating sparse matrix-vector multiplication in iterative methods using GPUSubhajit Sahu
Kiran Kumar Matam; Kishore Kothapalli
All Authors
19
Paper
Citations
1
Patent
Citation
486
Full
Text Views
Multiplying a sparse matrix with a vector (spmv for short) is a fundamental operation in many linear algebra kernels. Having an efficient spmv kernel on modern architectures such as the GPUs is therefore of principal interest. The computational challenges that spmv poses are significantlydifferent compared to that of the dense linear algebra kernels. Recent work in this direction has focused on designing data structures to represent sparse matrices so as to improve theefficiency of spmv kernels. However, as the nature of sparseness differs across sparse matrices, there is no clear answer as to which data structure to use given a sparse matrix. In this work, we address this problem by devising techniques to understand the nature of the sparse matrix and then choose appropriate data structures accordingly. By using our technique, we are able to improve the performance of the spmv kernel on an Nvidia Tesla GPU (C1060) by a factor of up to80% in some instances, and about 25% on average compared to the best results of Bell and Garland [3] on the standard dataset (cf. Williams et al. SC'07) used in recent literature. We also use our spmv in the conjugate gradient method and show an average 20% improvement compared to using HYB spmv of [3], on the dataset obtained from the The University of Florida Sparse Matrix Collection [9].
Published in: 2011 International Conference on Parallel Processing
Date of Conference: 13-16 Sept. 2011
Date Added to IEEE Xplore: 17 October 2011
ISBN Information:
ISSN Information:
INSPEC Accession Number: 12316254
DOI: 10.1109/ICPP.2011.82
Publisher: IEEE
Conference Location: Taipei City, Taiwan
Concurrent Matrix Multiplication on Multi-core ProcessorsCSCJournals
With the advent of multi-cores every processor has built-in parallel computational power and that can only be fully utilized only if the program in execution is written accordingly. This study is a part of an on-going research for designing of a new parallel programming model for multi-core architectures. In this paper we have presented a simple, highly efficient and scalable implementation of a common matrix multiplication algorithm using a newly developed parallel programming model SPC3 PM for general purpose multi-core processors. From our study it is found that matrix multiplication done concurrently on multi-cores using SPC3 PM requires much less execution time than that required using the present standard parallel programming environments like OpenMP. Our approach also shows scalability, better and uniform speedup and better utilization of available cores than that the algorithm written using standard OpenMP or similar parallel programming tools. We have tested our approach for up to 24 cores with different matrices size varying from 100 x 100 to 10000 x 10000 elements. And for all these tests our proposed approach has shown much improved performance and scalability
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
The large-scale cyberinformatics method to replication is defined not only by the analysis of local-area networks, but also by the structured need for the Internet. Here, we confirm the refinement of superpages, which embodies the unfortunate principles of operating systems. SHODE, our new methodology for secure methodologies, is the solution to all of these obstacles.
IRJET- Machine Learning Techniques for Code OptimizationIRJET Journal
This document summarizes research on using machine learning techniques for code optimization. It discusses how machine learning can help address two main compiler optimization problems: optimization selection and phase ordering. It provides an overview of supervised and unsupervised machine learning approaches that have been used, including linear models, decision trees, clustering, and evolutionary algorithms. Key papers applying these techniques to problems like optimization selection, phase ordering, and code compression are summarized. The document concludes that machine learning is increasingly being applied to compiler optimization problems to develop intelligent heuristics with minimal human input.
Comparative analysis of algorithms classification and methods the presentatio...Sayed Rahman
The document discusses modifications made to classification algorithms and text pre-processing methods to improve categorization of texts. It analyzes the naive Bayes, SVM, and PrTFIDF algorithms, and proposes modifications like incorporating class inclusion probabilities for naive Bayes, adjusting token weights for SVM, and developing a faster syntactic parser for pre-processing. Preliminary experiments showed these modifications improved classification accuracy compared to the original algorithms.
Comparative analysis of algorithms_MADISayed Rahman
The document summarizes research comparing algorithms and methods for categorizing texts. It discusses modifications made to naive Bayes, SVM, and PrTFIDF algorithms to improve classification accuracy. Preliminary processing steps like morphological analysis and parsing were also explored. Experimental results on two test collections showed the modified Bayesian algorithm achieved the best accuracy at 45.46%, outperforming PrTFIDF and SVM. Further areas of potential improvement are identified.
AUTOMATIC QUESTION GENERATION USING NATURAL LANGUAGE PROCESSINGIRJET Journal
The document describes a proposed method for automatic question generation using natural language processing and T5 text-to-text transfer transformer models. The method uses T5 models trained on the Stanford Question Answering Dataset to generate questions from paragraphs of text without requiring extensive grammar rules. The proposed system aims to assist students in learning by generating questions to test their understanding from provided materials.
An Adjacent Analysis of the Parallel Programming Model Perspective: A SurveyIRJET Journal
This document provides an overview and analysis of parallel programming models. It begins with an abstract discussing the growing demand for parallel computing and challenges with existing parallel programming frameworks. It then reviews several relevant studies on parallel programming models and architectures. The document goes on to describe several key parallel programming models in more detail, including the Parallel Random Access Machine (PRAM) model, Unrestricted Message Passing (UMP) model, and Bulk Synchronous Parallel (BSP) model. It discusses aspects of each model like architecture, communication methods, and associated cost models. The overall goal is to compare benefits and limitations of different parallel programming models.
This document discusses machine learning algorithms and their suitability for parallelization using MapReduce. It begins by introducing machine learning and different types of algorithms, including supervised, unsupervised, and reinforcement learning. It then discusses how several common machine learning algorithms can be expressed using the MapReduce framework. Single-pass algorithms like language modeling and naive Bayes classification are well-suited since they involve extracting statistics from each data point. Iterative algorithms can also be parallelized by chaining multiple MapReduce jobs, though the map tasks may need access to global parameters. Overall, the document analyzes how different machine learning algorithms map to the data processing patterns of MapReduce.
A Program Transformation Technique to Support Aspect-Oriented Programming wit...Sabrina Ball
This document proposes a technique for applying aspect-oriented programming to C++ templates using program transformation. It discusses the challenges of combining aspects with templates, as templates can be instantiated in multiple places but an aspect may only be desired for a subset. The technique uses a source-to-source preprocessor and program transformation system to weave advice into template implementations. It presents an example applying the technique to a scientific computing library to modularize crosscutting concerns in template-based code.
INTEGRATION OF LATEX FORMULA IN COMPUTER-BASED TEST APPLICATION FOR ACADEMIC ...IJCSES Journal
LaTeX is a free document preparation system that handles the typesetting of mathematical expressions
smoothly and elegantly. It has become the standard format for creating and publishing research articles in
mathematics and many scientific fields. Computer-based testing (CBT) has become widespread in recent
years. Most establishments now use it to deliver assessments as an alternative to using the pen- paper
method. To deliver an assessment, the examiner would first add a new exam or edit an existing exam using
a CBT editor. Thus, the implementation of CBT should comprise both support for setting and administering
questions. Existing CBT applications used in the academic space lacks the capacity to handle advanced
formulas, programming codes, and tables, thereby resorting to converting them into images which takes a
lot of time and storage space. In this paper, we discuss how we solvde this problem by integrating latex
technology into our CBT applications. This enables seamless manipulation and accurate rendering of
tables, programming codes, and equations to increase readability and clarity on both the setting and
administering of questions platforms. Furthermore, this implementation has reduced drastically the sizes of
system resources allocated to converting tables, codes, and equations to images. Those in mathematics,
statistics, computer science, engineering, chemistry, etc. will find this application useful.
Malayalam Word Sense Disambiguation using Machine Learning ApproachIRJET Journal
This paper proposes a Malayalam word sense disambiguation system using a support vector machine approach. The major parts of the work include creating a WSD dataset in Malayalam and training a machine learning model on both stemmed and unstemmed data. Accuracy was highest when using unstemmed data. Accuracy also increased with larger training datasets, except for one ambiguous word. The proposed system involves preprocessing data, part-of-speech tagging, vectorization, and using an SVM classifier. Evaluation on three polysemous words showed accuracy ranging from 65-85% depending on the word and dataset size.
IRJET - Automated Essay Grading System using Deep LearningIRJET Journal
This document describes an automated essay grading system that uses deep learning techniques. It discusses how previous grading systems used machine learning algorithms like linear regression and support vector machines. It then presents a new system that uses an LSTM and dense neural network model to grade essays on a scale of 1-10. The system preprocesses essays by removing stopwords and numbers before converting the text to word vectors as input to the deep learning model. It aims to reduce the time spent on grading large numbers of essays compared to manual grading.
Dominant block guided optimal cache size estimation to maximize ipc of embedd...ijesajournal
Embedded system software is highly constrained from performance, memory footprint, energy consumption and implementing cost view point. It is always desirable to obtain better Instructions per Cycle (IPC). Instruction cache has major contribution in improving IPC. Cache memories are realized on the same chip where the processor is running. This considerably increases the system cost as well. Hence, it is required to maintain a trade-off between cache sizes and performance improvement offered. Determining the number of cache lines and size of cache line are important parameters for cache designing. The design space for cache is quite large. It is time taking to execute the given application with different cache sizes on an instruction set simulator (ISS) to figure out the optimal cache size. In this paper, a technique is proposed to identify a number of cache lines and cache line size for the L1 instruction cache that will offer best or nearly best IPC. Cache size is derived, at a higher abstraction level, from basic block analysis in the Low Level Virtual Machine (LLVM) environment. The cache size estimated from the LLVM environment is cross validated by simulating the set of benchmark applications with different cache sizes in SimpleScalar’s out-of-order simulator. The proposed method seems to be superior in terms of estimation accuracy and/or estimation time as compared to the existing methods for estimation of optimal cache size parameters (cache line size, number of cache lines).
Dominant block guided optimal cache size estimation to maximize ipc of embedd...ijesajournal
Embedded system software is highly constrained from performance, memory footprint, energy consumption
and implementing cost view point. It is always desirable to obtain better Instructions per Cycle (IPC).
Instruction cache has major contribu
tion in improving IPC. Cache memories are realized on the same chip
where the processor is running. This considerably increases the system cost as well. Hence, it is required to
maintain a trade
-
off between cache sizes and performance improvement offered.
Determining the number
of cache lines and size of cache line are important parameters for cache designing. The design space for
cache is quite large. It is time taking to execute the given application with different cache sizes on an
instruction set simula
tor (ISS) to figure out the optimal cache size. In this paper, a technique is proposed to
identify a number of cache lines and cache line size for the L1 instruction cache that will offer best or
nearly best IPC. Cache size is derived, at a higher abstract
ion level, from basic block analysis in the Low
Level Virtual Machine (LLVM) environment. The cache size estimated from the LLVM environment is cross
validated by simulating the set of benchmark applications with different cache sizes in SimpleScalar’s out
-
of
-
order simulator. The proposed method seems to be superior in terms of estimation accuracy and/or
estimation time as compared to the existing methods for estimation of optimal cache size parameters (cache
line size, number of cache lines).
The document presents an ensemble model for chunking natural language text that combines a transformer model (RoBERTa) with a bidirectional LSTM and CNN model. The authors train these models on common chunking datasets like CoNLL 2000 and English Penn Treebank. They find that by using an ensemble of the transformer and RNN-CNN models, which compensate for each other's weaknesses, they are able to achieve state-of-the-art results on chunking, with an F1 score of 97.3% on CoNLL 2000, exceeding previous work. The transformer model provides attention-based contextual embeddings while the RNN-CNN model uses custom embeddings including POS tags to improve accuracy on tags that the transformer model struggles with.
This document summarizes a research paper that proposes a new heuristic called PAUSE for investigating the producer-consumer problem in distributed systems. The paper motivates the need to study this problem, describes PAUSE's approach of using compact configurations and decentralized components, outlines its implementation in Lisp and Java, and presents experimental results showing PAUSE outperforms previous methods. Related work investigating similar challenges is also discussed.
This document discusses the process of lexical analysis in compiling or interpreting a program. It begins with an abstract discussing how lexical analysis involves turning a string of letters into tokens like keywords, identifiers, constants, and operators. It then provides background on lexical analysis, explaining that it reads input characters one by one and groups them into tokens that are passed to a parser. Key techniques for lexical analysis mentioned include using regular expressions and finite automata to identify tokens. The document also reviews related work on parallelizing lexical analysis and includes diagrams of the lexical analysis process and sample output tokens. It concludes by discussing limitations and opportunities for future work improving lexical analysis.
This document proposes a novel framework called smooth sparse coding for learning sparse representations of data. It incorporates feature similarity or temporal information present in data sets via non-parametric kernel smoothing. The approach constructs codes that represent neighborhoods of samples rather than individual samples, leading to lower reconstruction error. It also proposes using marginal regression rather than lasso for obtaining sparse codes, providing a dramatic speedup of up to two orders of magnitude without sacrificing accuracy. The document contributes a framework for incorporating domain information into sparse coding, sample complexity results for dictionary learning using smooth sparse coding, an efficient marginal regression training procedure, and successful application to classification tasks with improved accuracy and speed.
This paper presentsa novel data flow architecturethat utilizes data from engineering simulations to
generate a reduced order model within Apache Spark. The reduced order model from Spark is then utilized by
anevolutionary algorithm in the optimization of an industrial system component. This work is presented in the
context of the shape optimization of a heat exchanger fin and demonstrates the ability of theengineering
simulation, the reduced order model and the evolutionary algorithm to exchange data with each other by
utilizing Spark as the common data-processing framework. In order to enable a user to monitor the input design
parameter space,self-organizing maps are generated for visualization. The results of theevolutionary
optimization utilizing this data flow are compared with results from invoking high-fidelity engineering
simulations. This novel data flow architecture decouples the evolutionary algorithm from the reduced order
model and allows improvement of the optimization results by continuously augmenting the reduced order model
with data from the evolutionary algorithm.Additionally, when constraints on the optimization algorithm are
modifiedthe evolutionary algorithm canadapt and evolve good solutions. Themethodology presented in this
articlealso makes it feasible to simultaneously tune evolutionary optimization experiments along with
engineering simulations at a relatively low computational cost.
AN IMPROVED MT5 MODEL FOR CHINESE TEXT SUMMARY GENERATIONijaia
Complicated policy texts require a lot of effort to read, so there is a need for intelligent interpretation of
Chinese policies. To better solve the Chinese Text Summarization task, this paper utilized the mT5 model
as the core framework and initial weights. Additionally, In addition, this paper reduced the model size
through parameter clipping, used the Gap Sentence Generation (GSG) method as unsupervised method,
and improved the Chinese tokenizer. After training on a meticulously processed 30GB Chinese training
corpus, the paper developed the enhanced mT5-GSG model. Then, when fine-tuning the Chinese Policy
text, this paper chose the idea of “Dropout Twice”, and innovatively combined the probability distribution
of the two Dropouts through the Wasserstein distance. Experimental results indicate that the proposed
model achieved Rouge-1, Rouge-2, and Rouge-L scores of 56.13%, 45.76%, and 56.41% respectively on
the Chinese policy text summarization dataset.
Entering College Essay. Top 10 Tips For College AdmissNat Rice
The document summarizes the discovery of a new species, Alborum Plumae, found in Mount Kosciuszko National Park in Australia. Dr. Ella Beard discovered small feathered creatures perfectly camouflaged as snow lumps on tree branches. Early analysis finds they spend most of their time stationary to avoid detection, using their wings only for escaping predators. While sharing traits with birds like down insulation and wing-aided flight, further research is needed to determine their exact taxonomic classification.
006 Essay Example First Paragraph In An ThatsnoNat Rice
The document summarizes racism depicted in two novels by John Updike: Rabbit Run and Rabbit Redux. It notes that both books show racism as an issue, with characters making racist comments about black people. Specific quotes are presented that demonstrate racist language used by characters towards black individuals. The racism portrayed is less extreme in Rabbit Redux compared to Rabbit Run. Overall, the document analyzes how Updike explores themes of racism through his famous character Harry Rabbit and the other characters in these two novels from his Rabbit series.
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The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
The large-scale cyberinformatics method to replication is defined not only by the analysis of local-area networks, but also by the structured need for the Internet. Here, we confirm the refinement of superpages, which embodies the unfortunate principles of operating systems. SHODE, our new methodology for secure methodologies, is the solution to all of these obstacles.
IRJET- Machine Learning Techniques for Code OptimizationIRJET Journal
This document summarizes research on using machine learning techniques for code optimization. It discusses how machine learning can help address two main compiler optimization problems: optimization selection and phase ordering. It provides an overview of supervised and unsupervised machine learning approaches that have been used, including linear models, decision trees, clustering, and evolutionary algorithms. Key papers applying these techniques to problems like optimization selection, phase ordering, and code compression are summarized. The document concludes that machine learning is increasingly being applied to compiler optimization problems to develop intelligent heuristics with minimal human input.
Comparative analysis of algorithms classification and methods the presentatio...Sayed Rahman
The document discusses modifications made to classification algorithms and text pre-processing methods to improve categorization of texts. It analyzes the naive Bayes, SVM, and PrTFIDF algorithms, and proposes modifications like incorporating class inclusion probabilities for naive Bayes, adjusting token weights for SVM, and developing a faster syntactic parser for pre-processing. Preliminary experiments showed these modifications improved classification accuracy compared to the original algorithms.
Comparative analysis of algorithms_MADISayed Rahman
The document summarizes research comparing algorithms and methods for categorizing texts. It discusses modifications made to naive Bayes, SVM, and PrTFIDF algorithms to improve classification accuracy. Preliminary processing steps like morphological analysis and parsing were also explored. Experimental results on two test collections showed the modified Bayesian algorithm achieved the best accuracy at 45.46%, outperforming PrTFIDF and SVM. Further areas of potential improvement are identified.
AUTOMATIC QUESTION GENERATION USING NATURAL LANGUAGE PROCESSINGIRJET Journal
The document describes a proposed method for automatic question generation using natural language processing and T5 text-to-text transfer transformer models. The method uses T5 models trained on the Stanford Question Answering Dataset to generate questions from paragraphs of text without requiring extensive grammar rules. The proposed system aims to assist students in learning by generating questions to test their understanding from provided materials.
An Adjacent Analysis of the Parallel Programming Model Perspective: A SurveyIRJET Journal
This document provides an overview and analysis of parallel programming models. It begins with an abstract discussing the growing demand for parallel computing and challenges with existing parallel programming frameworks. It then reviews several relevant studies on parallel programming models and architectures. The document goes on to describe several key parallel programming models in more detail, including the Parallel Random Access Machine (PRAM) model, Unrestricted Message Passing (UMP) model, and Bulk Synchronous Parallel (BSP) model. It discusses aspects of each model like architecture, communication methods, and associated cost models. The overall goal is to compare benefits and limitations of different parallel programming models.
This document discusses machine learning algorithms and their suitability for parallelization using MapReduce. It begins by introducing machine learning and different types of algorithms, including supervised, unsupervised, and reinforcement learning. It then discusses how several common machine learning algorithms can be expressed using the MapReduce framework. Single-pass algorithms like language modeling and naive Bayes classification are well-suited since they involve extracting statistics from each data point. Iterative algorithms can also be parallelized by chaining multiple MapReduce jobs, though the map tasks may need access to global parameters. Overall, the document analyzes how different machine learning algorithms map to the data processing patterns of MapReduce.
A Program Transformation Technique to Support Aspect-Oriented Programming wit...Sabrina Ball
This document proposes a technique for applying aspect-oriented programming to C++ templates using program transformation. It discusses the challenges of combining aspects with templates, as templates can be instantiated in multiple places but an aspect may only be desired for a subset. The technique uses a source-to-source preprocessor and program transformation system to weave advice into template implementations. It presents an example applying the technique to a scientific computing library to modularize crosscutting concerns in template-based code.
INTEGRATION OF LATEX FORMULA IN COMPUTER-BASED TEST APPLICATION FOR ACADEMIC ...IJCSES Journal
LaTeX is a free document preparation system that handles the typesetting of mathematical expressions
smoothly and elegantly. It has become the standard format for creating and publishing research articles in
mathematics and many scientific fields. Computer-based testing (CBT) has become widespread in recent
years. Most establishments now use it to deliver assessments as an alternative to using the pen- paper
method. To deliver an assessment, the examiner would first add a new exam or edit an existing exam using
a CBT editor. Thus, the implementation of CBT should comprise both support for setting and administering
questions. Existing CBT applications used in the academic space lacks the capacity to handle advanced
formulas, programming codes, and tables, thereby resorting to converting them into images which takes a
lot of time and storage space. In this paper, we discuss how we solvde this problem by integrating latex
technology into our CBT applications. This enables seamless manipulation and accurate rendering of
tables, programming codes, and equations to increase readability and clarity on both the setting and
administering of questions platforms. Furthermore, this implementation has reduced drastically the sizes of
system resources allocated to converting tables, codes, and equations to images. Those in mathematics,
statistics, computer science, engineering, chemistry, etc. will find this application useful.
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Dominant block guided optimal cache size estimation to maximize ipc of embedd...ijesajournal
Embedded system software is highly constrained from performance, memory footprint, energy consumption and implementing cost view point. It is always desirable to obtain better Instructions per Cycle (IPC). Instruction cache has major contribution in improving IPC. Cache memories are realized on the same chip where the processor is running. This considerably increases the system cost as well. Hence, it is required to maintain a trade-off between cache sizes and performance improvement offered. Determining the number of cache lines and size of cache line are important parameters for cache designing. The design space for cache is quite large. It is time taking to execute the given application with different cache sizes on an instruction set simulator (ISS) to figure out the optimal cache size. In this paper, a technique is proposed to identify a number of cache lines and cache line size for the L1 instruction cache that will offer best or nearly best IPC. Cache size is derived, at a higher abstraction level, from basic block analysis in the Low Level Virtual Machine (LLVM) environment. The cache size estimated from the LLVM environment is cross validated by simulating the set of benchmark applications with different cache sizes in SimpleScalar’s out-of-order simulator. The proposed method seems to be superior in terms of estimation accuracy and/or estimation time as compared to the existing methods for estimation of optimal cache size parameters (cache line size, number of cache lines).
Dominant block guided optimal cache size estimation to maximize ipc of embedd...ijesajournal
Embedded system software is highly constrained from performance, memory footprint, energy consumption
and implementing cost view point. It is always desirable to obtain better Instructions per Cycle (IPC).
Instruction cache has major contribu
tion in improving IPC. Cache memories are realized on the same chip
where the processor is running. This considerably increases the system cost as well. Hence, it is required to
maintain a trade
-
off between cache sizes and performance improvement offered.
Determining the number
of cache lines and size of cache line are important parameters for cache designing. The design space for
cache is quite large. It is time taking to execute the given application with different cache sizes on an
instruction set simula
tor (ISS) to figure out the optimal cache size. In this paper, a technique is proposed to
identify a number of cache lines and cache line size for the L1 instruction cache that will offer best or
nearly best IPC. Cache size is derived, at a higher abstract
ion level, from basic block analysis in the Low
Level Virtual Machine (LLVM) environment. The cache size estimated from the LLVM environment is cross
validated by simulating the set of benchmark applications with different cache sizes in SimpleScalar’s out
-
of
-
order simulator. The proposed method seems to be superior in terms of estimation accuracy and/or
estimation time as compared to the existing methods for estimation of optimal cache size parameters (cache
line size, number of cache lines).
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generate a reduced order model within Apache Spark. The reduced order model from Spark is then utilized by
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context of the shape optimization of a heat exchanger fin and demonstrates the ability of theengineering
simulation, the reduced order model and the evolutionary algorithm to exchange data with each other by
utilizing Spark as the common data-processing framework. In order to enable a user to monitor the input design
parameter space,self-organizing maps are generated for visualization. The results of theevolutionary
optimization utilizing this data flow are compared with results from invoking high-fidelity engineering
simulations. This novel data flow architecture decouples the evolutionary algorithm from the reduced order
model and allows improvement of the optimization results by continuously augmenting the reduced order model
with data from the evolutionary algorithm.Additionally, when constraints on the optimization algorithm are
modifiedthe evolutionary algorithm canadapt and evolve good solutions. Themethodology presented in this
articlealso makes it feasible to simultaneously tune evolutionary optimization experiments along with
engineering simulations at a relatively low computational cost.
AN IMPROVED MT5 MODEL FOR CHINESE TEXT SUMMARY GENERATIONijaia
Complicated policy texts require a lot of effort to read, so there is a need for intelligent interpretation of
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text, this paper chose the idea of “Dropout Twice”, and innovatively combined the probability distribution
of the two Dropouts through the Wasserstein distance. Experimental results indicate that the proposed
model achieved Rouge-1, Rouge-2, and Rouge-L scores of 56.13%, 45.76%, and 56.41% respectively on
the Chinese policy text summarization dataset.
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Automated Essay Scoring Using Efficient Transformer-Based Language Models
1. arXiv:2102.13136v1
[cs.CL]
25
Feb
2021
Automated essay scoring using efficient transformer-based language
models
Christopher M. Ormerod, Akanksha Malhotra, and Amir Jafari
ABSTRACT. Automated Essay Scoring (AES) is a cross-disciplinary effort involving Education,
Linguistics, and Natural Language Processing (NLP). The efficacy of an NLP model in AES tests
it ability to evaluate long-term dependencies and extrapolate meaning even when text is poorly
written. Large pretrained transformer-based language models have dominated the current state-
of-the-art in many NLP tasks, however, the computational requirements of these models make
them expensive to deploy in practice. The goal of this paper is to challenge the paradigm in NLP
that bigger is better when it comes to AES. To do this, we evaluate the performance of several
fine-tuned pretrained NLP models with a modest number of parameters on an AES dataset. By
ensembling our models, we achieve excellent results with fewer parameters than most pretrained
transformer-based models.
1. Introduction
The idea that a computer could analyze writing style dates back to the work of Page in
1968 [31]. Many engines in production today rely on explicitly defined hand-crafted features
designed by experts to measure the intrinsic characteristics of writing [14]. These features are
combined with frequency-based methods and statistical models to form a collection of methods
that are broadly termed Bag-of-Word (BOW) methods [49]. While BOW methods have been
very successful from a purely statistical standpoint, [22] showed they tend to be brittle with
respect novel uses of language and vulnerable to adversarially crafted inputs.
Neural Networks learn features implicitly rather than explicitly. It has been shown that ini-
tial neural network AES engines tend to be more more accurate and more robust to gaming
than BOW methods [12, 15]. In the broader NLP community, the recurrent neural network
approaches used have been replaced by transformer-based approaches, like the Bidirectional En-
coder Representations from Transformers (BERT) [13]. These models tend to possess an order
of magnitude more parameters than recurrent neural networks, but also boast state-of-the-art re-
sults in the General Language Understanding Evaluation (GLUE) benchmarks [42, 45]. One of
the main problems in deploying these models is their computational and memory requirements
[28]. This study explores the effectiveness of efficient versions of transformer-based models in
the domain of AES. There are two aspects of AES that distinguishes it from GLUE tasks that
might benefit from the efficiencies introduced in these models; firstly, the text being evaluated
can be almost arbitrarily long. Secondly, essays written by students often contain many more
spelling issues than would be present in typical GLUE tasks. It could be the case that fewer
1
2. 2 CHRISTOPHER M. ORMEROD, AKANKSHA MALHOTRA, AND AMIR JAFARI
and more often updated parameters might possibly be better in this situation or more generally
where smaller training sets are used.
With respect to essay scoring the Automated Student Assessment Prize (ASAP) AES data-
set on Kaggle is a standard openly accessible data-set by which we may evaluate the performance
of a given AES model [35]. Since the original test set is no longer available, we use the five-fold
validation split presented in [46] for a fair and accurate comparison. The accuracy of BERT and
XLNet have been shown to be very solid on the Kaggle dataset [33, 47]. To our knowledge,
combining BERT with hand-crafted features form the current state-of-the-art [40].
The recent works of have challenged the paradigm that bigger models are necessarily better
[11, 21, 24, 29]. The models in these papers possess some fundamental architectural character-
istics that allow for a drastic reduction in model size, some of which may even be an advantage
in AES. For this study, we consider the performance of the Albert models [26], Reformer mod-
els [21], a version of the Electra model [11], and the Mobile-BERT model [39] on the ASAP
AES data-set. Not only are each of these models more efficient, we show that simple ensembles
provide the best results to our knowledge on the ASAP AES dataset.
There are several reasons that this study is important. Firstly, the size of models scale
quadratically with maximum length allowed, meaning that essays may be longer than the max-
imal length allowed by most pretrained transformer-based models. By considering efficiencies
in underlying transformer architectures we can work on extending that maximum length. Sec-
ondly, as noted by [28], one of the barriers to effectively putting these models in production is
the memory and size constraints of having fine-tuned models for every essay prompt. Lastly, we
seek models that impose a smaller computational expense, which in turn has been linked by [37]
to a much smaller carbon footprint.
2. Approaches to Automated Essay Scoring
From an assessment point of view, essay tests are useful in evaluating a student’s ability
to analyze and synthesize information, which assesses the upper levels of Blooms Taxonomy.
Many modern rubrics use a multitude of scoring dimensions to evaluate an essay, including
organization, elaboration, and writing style. An AES engine is a model that assigns scores to a
piece of text closely approximating the way a person would score the text as a final score or in
multiple dimensions.
To evaluate the performance of an engine we use standard inter-rater reliability statistics.
It has become standard practice in the development of a training set for an AES engine that
each essay is evaluated by two different raters from which we may obtain a resolved score. The
resolved score is usually either the same as the two raters in the case that they agree and an
adjudicated score in cases in which they do not. In the case of the ASAP AES data-set, the
resolved score for some items is taken to be the sum of the two raters, and a resolved score
for others. The goal of a good model is to have a higher agreement with the resolved score in
comparison with the agreement two raters have with each other. The most widely used statistic
used to evaluate the agreement of two different collections of scores is the quadratic weighted
kappa (QWK), defined by
(1) κ =
P P
wijxij
P P
wijmij
3. AUTOMATED ESSAY SCORING USING EFFICIENT TRANSFORMER-BASED LANGUAGE MODELS 3
where xi,j is the observed probability
mi,j = xij(1 − xij),
and
wij = 1 −
(i − j)2
(k − 1)2
,
where k is the number of classes. The other measurements used in the industry are the standard-
ized mean difference (SMD) and the exact match or accuracy (Acc). The total number of essays
and the human-human agreement for the two raters and the score ranges are shown in table 1.
The usual protocol for training a statistical model is that some portion of the training set is
set aside for evaluation while the remaining set is used for training. A portion of the training
set is often isolated for purposes of early stopping and hyperparameter tuning. In evaluating the
Kaggle dataset, we use the 5-fold cross validation splits defined by [46] so that our results are
comparable to other works [1, 12, 33, 40, 47]. The resulting QWK is defined to be the average
of the QWK values on each of the five different splits.
Essay Prompt
1 2 3 4 5 6 7 8
Rater score range 1-6 1-6 0-3 0-3 0-4 0-4 0-12 5-30
Resolved score
range
2-12 1-6 0-3 0-3 0-4 0-4 2-24 10-60
Average Length 350 350 150 150 150 150 250 650
Training examples 1783 1800 1726 1772 1805 1800 1569 723
QWK 0.721 0.814 0769 0.851 0.753 0.776 0.721 0.624
Acc 65.3% 78.3% 74.9% 77.2% 58.0% 62.3% 29.2% 27.8%
SMD 0.008 0.027 0.055 0.005 0.001 0.011 0.006 0.069
TABLE 1. A summary of the Automated Student Assessment Prize Automated
Essay Scoring data-set.
3. Methodology
Most engines currently in production rely on Latent Semantic Analysis or a multitude of
hand-crafted features that measure style and prose. Once sufficiently many features are com-
piled, a traditional machine learning classifier, like logistic regression, is applied and fit to a
training corpus. As a representative of this class of model we include the results of the “En-
hanced AI Scoring Engine”, which is open source 1
.
When modelling language with neural networks, the first layer of most neural networks are
embedding layers, which send every token to an element of a semantic vector space. When
training a neural network model from scratch, the word embedding vocabulary often comprises
of the set of tokens that appear in the training set. There are several problems with this ap-
proach that come as a result of word sparsity in language and the presence of spelling mistakes.
Alternatively, one may use a pretrained word embedding [30] built from a large corpus with a
large vocabulary with a sufficiently high dimensional semantic vector space. From an efficiency
1https://github.com/edx/ease
4. 4 CHRISTOPHER M. ORMEROD, AKANKSHA MALHOTRA, AND AMIR JAFARI
standpoint, these word embeddings alone can account for billions of parameters. We can shrink
our embedding and address some issues arising from word sparsity by fixing a vocabulary of
subwords using a version of the byte-pair-encoding (BPE) [25, 34]. As such, pretrained models
like BERT and those we use in this study utilize subwords to fix the size of the vocabulary.
In addition to the word embeddings, positional embeddings and segment embedding are
used to give the model information about the position of each word and next sentences prediction.
The combination of the 3 embedding are the keys to reduce the vocabulary size, handling the out
of vocab, and preserving the order of words.
Once we have applied the embedding layer to the tokens of a piece of text, it is essentially a
sequence of elements of a vector space, which can be represented by a matrix whose dimensions
are governed by the size of the semantic vector space and the number of tokens in the text.
In the field of language modeling, sequence-to-sequence models (seq2seq) in the form of this
paper were proposed in [38]. Initially, seq2seq models were used for natural machine translation
between multiple languages. The seq2seq model has an encoder-decoder component; an encoder
analyzes the input sequence and creates a context vector while the decoder is initialized with the
context vector and is trainer to produce the transformed output. Previous language models were
based on a fixed-length context vector and suffered from an inability to infer context over long
sentences and text in general. An attention mechanism was used to improve the performance in
translation for long sentences.
The use of a self-attention mechanism turns out to be very useful in th context of machine
translation [3]. This mechanism, and it various derivatives, have been responsible for a large
number of accuracy gains over a wide range of NLP tasks more broadly. The form of attention
for this paper can be found in [41]. Given a query matrix Q, key matrix K, and value matrix V ,
then the resulting sequence is given by
(2) Attention(Q, K, V ) = softmax
QKT
√
dk
V.
These matrices are obtained by linear transformations of either the output of a neural network,
the output of a previous attention mechanism, or embeddings. The overall success of atten-
tion has led to the development of the transformer [41]. The architecture of the transformer is
outlined in Figure 1.
In the context of efficient models, if we consider all sequences up to length L and if each
query is of size d, then each keys are also of length d, hence, the matrix QKT of size L×L. The
implication is that the memory and computational power required to implement this mechanism
scales quadratically with length. The above-mentioned models often adhere to a size restriction
by letting L = 512.
4. Efficient Language Models
The transformer in language modeling is a innovative architecture to solve issues of seq2seq
tasks while handling long term dependencies. It relies on self-attention mechanisms in its net-
works architecture. Self attention is in charge of managing the interdependence within the input
elements.
In this study, we use five prominent models all of which are known to perform well as
language models and possess an order of magnitude fewer parameters than BERT. It should be
noted that many of the other models, like RoBERTa, XLNet, GPT models, T5, XLM, and even
5. AUTOMATED ESSAY SCORING USING EFFICIENT TRANSFORMER-BASED LANGUAGE MODELS 5
Embedding
Position +
Multi-head
Attention
Add Norm
Feed
Forward
Add Norm
Embedding
Position
+
Masked
Multi-head
Attention
Add Norm
Multi-head
Attention
Add Norm
Feed
Forward
Add Norm
N×
N×
FIGURE 1. This is the basic architecture of a transformer-based model. The
left block of N layers is called the encoder while the right block of N layers is
called the decoder. The output of the Decoder is a sequence.
Distilled BERT all possess more than 60M parameters, hence, were excluded for this study. We
only consider models utilizing an innovation that drastically reduces the number of parameters
to at most one quarter the number of parameters of BERT. Of this class of models, we present a
list of models and their respective sizes in Table 2.
The backbone architecture of all above language models is BERT [13]. It has become the
state-of-the-art model for many different Natural Language Undestanding (NLU), Natural Lan-
guage Generation (NLG) tasks including sequence and document classification.
The first models we consider are the Albert models of [26]. The first efficiency of Albert is
that the embedding matrix is factored. In the BERT model, the embedding size must be the same
as the hidden layer size. Since the vocabulary of the BERT model is approximately 30,000 and
the hidden layer size is 768 in the base model, the embedding alone requires approximately 25M
parameters to define. Not only does this significantly increase the size of the model, we expect
that some of those parameters to by updated rarely during the training process. By applying
a linear layer after the embedding, we effectively factor the embedding matrix in a way that
the actual embedding size can be much smaller than the feed forward layer. In the two models
(large and base), the size of the vocabulary is about the same however the embedding dimension
is effectively 128 making the embedding matrix one sixth the size.
6. 6 CHRISTOPHER M. ORMEROD, AKANKSHA MALHOTRA, AND AMIR JAFARI
Model Number Training Inference
of Time Time
Parameters Speedup Speedup
BERT (base) 110M 1.0× 1.0×
Albert (base) 12M 1.0× 1.0×
Albert (large) 18M 0.7× 0.6×
Electra (small) 14M 3.8× 2.4×
Mobile BERT 24M 2.5× 1.7×
Reformer 16M 2.2× 1.6×
Electra + Mobile-BERT 38M 1.5× 1.0×
TABLE 2. A summary of the memory and approximations of the computational
requirements of the models we use in the study when comparing to BERT. These
are estimates based on single epoch times using a fixed batch-size in training
and inference.
A second efficiency proposed by Albert is that the layers share parameters. [26] compare a
multitude of parameter sharing scenarios in which all their parameters are shared across layers.
The base and large Albert models, with 12 layer and 24 layers respectively, only possess a total
of 12M and 18M parameters. The hidden size of of the base and large models are also 768 and
1024 respectively. Increasing the number of layers does increase the number of parameters but
does come with a computational cost. In the pretraining of the Albert models, the models are
trained with a sentence order prediction (SOP) loss function instead of next sentence prediction.
It is argued by [26] that SOP can solve NSP tasks while the converse is not true and that this
distinction leads to consistent improvements in downstream tasks.
The second model we consider is the small version of Electra model presented by [11].
Like the Albert model, there is a linear layer between the embedding and the hidden layers,
allowing for a embedding size of 128, a hidden layer size of 256, and only four attention heads.
The pretraining of the Electra model is trained as a pair of neural networks consisting of a
generator and a discriminator with weight-sharing between the two networks. The generators
role is to replace tokens in a sequence, and is therefore trained as a masked language model. The
discriminator tries to identify that tokens were replaced by the generator in the sequence.
The third model, Mobile-BERT, was presented in [39]. This model uses the same embedding
factorization used to decouple the embedding size of 128 with the hidden size of 512. The main
innovation of [39] is that they decrease the model size by introducing a pair of linear transfor-
mations, called bottlenecks, around the transformer unit so that the transformer unit is operating
on a hidden size of 128 instead of the full hidden size of of 512. This effectively shrinks the
size of the underlying building blocks. MobileBERT uses absolute position embeddings and it
is efficient at predicting masked tokens and at NLU.
The Reformer architecture of [21] differs from BERT most substantially by its version of an
attention mechanism and that the feedforward component is of the attention layers use revsible
residual layers. This means that, like [18], the inputs of each layer can be computed on demand
instead of being stored. [21] noted that they use an approximation of the (2) in which the linear
transformation used to define Q and K are the same, i.e., Q = K. When we calculate QKT ,
more importantly, the softmax, we need only consider values in Q and K that are close. Using
7. AUTOMATED ESSAY SCORING USING EFFICIENT TRANSFORMER-BASED LANGUAGE MODELS 7
random vectors, we may create a Locally Sensitive Hashing (LSH) scheme, allowing us to chunk
key/query vectors into finite collections of vectors that are known to contribute to the softmax.
Each chunk may be computed in parallel changing the complexity from scaling with length as
O(L2) to O(L log L). This is essentially a way to utilize the sparse nature of the attention matrix,
attempting to not calculate pairs of values not likely to contribute to the softmax. More hashes
improves the hashing scheme and better approximates the attention mechanism.
5. Results
The networks above are all pretrained to be seq2seq models. While some of the pretraining
differs for some models, as discussed above, we are required to modify a sequence-to-sequence
neural network to produce a classification. Typically, the way in which this is done is that
we take the first vector of the sequence as a finite set of features from which we may apply a
classification. Applying a linear layer to these features produces a fixed length vector that is
used for classification.
QWK scores Prompt AVG
1 2 3 4 5 6 7 8 QWK
EASE 0.781 0.621 0.630 0.749 0.782 0.771 0.727 0.534 0.699
LSTM 0.775 0.687 0.683 0.795 0.818 0.813 0.805 0.594 0.746
LSTM+CNN 0.821 0.688 0.694 0.805 0.807 0.819 0.808 0.644 0.761
LSTM+CNN+Att 0.822 0.682 0.672 0.814 0.803 0.811 0.801 0.705 0.764
BERT(base) 0.792 0.680 0.715 0.801 0.806 0.805 0.785 0.596 0.758
XLNet 0.777 0.681 0.693 0.806 0.783 0.794 0.787 0.627 0.743
BERT + XLNet 0.808 0.697 0.703 0.819 0.808 0.815 0.807 0.605 0.758
R2
BERT 0.817 0.719 0.698 0.845 0.841 0.847 0.839 0.744 0.794
BERT + Features 0.852 0.651 0.804 0.888 0.885 0.817 0.864 0.645 0.801
Electra (small) 0.816 0.664 0.682 0.792 0.792 0.787 0.827 0.715 0.759
Albert (base) 0.807 0.671 0.672 0.813 0.802 0.816 0.826 0.700 0.763
Albert (large) 0.801 0.676 0.668 0.810 0.805 0.807 0.832 0.700 0.763
Mobile-BERT 0.810 0.663 0.663 0.795 0.806 0.808 0.824 0.731 0.762
Reformer 0.802 0.651 0.670 0.754 0.771 0.762 0.747 0.548 0.713
Electra+Mobile-BERT 0.823 0.683 0.691 0.805 0.808 0.802 0.835 0.748 0.774
Ensemble 0.831 0.679 0.690 0.825 0.817 0.822 0.841 0.748 0.782
TABLE 3. The first set of agreement (QWK) statistics for each prompt. EASE,
LSTM, LSTM+CNN, and LSTM+CNN+Att, were presented by [46] and [12].
The results of BERT, BERT extensions, and XLNET have been presented in
[33, 40, 47]. The remaining rows are the results of this paper.
Given a set of n possible scores, we divide the interval [0, 1] into n even sub-intervals and
map each score to the midpoint of those intervals. So in a similar manner to [46], we treat this
classification as a regression problem with a loss function of the mean squared error. This is
slightly different from the standard multilabel classification using a Cross-entropy loss function
often applied by default to transformer-based classification problems. Using the standard pre-
trained models with an untrained linear classification layer with a sigmoid activation function,
we applied a small grid-search using two learning rates and two batch sizes for each model. The
model performing the best on the test set was applied to validation.
8. 8 CHRISTOPHER M. ORMEROD, AKANKSHA MALHOTRA, AND AMIR JAFARI
For the reformer model, we pretrained our own 6-layer reformer model using a hidden layer
size of 512, 4 hashing functions, and 4 attention heads. We used a cased sentencepiece tok-
enization consisting of 16,000 subwords and the model was trained with a maximum length of
1024 on a large corpus of essay texts from various grades on a single Nvidia RTX8000. This
model addresses the length constraints other essay models struggle with on transformer-based
architectures.
We see that both Electra and Mobile-BERT show performance higher than BERT itself
despite being smaller and faster. Given the extensive hyper-parameter tuning performed in [33],
we can only speculate that any additional gains may be due to architectural differences.
Lastly, we took our best models and simply averaged the outputs to obtain an ensembles
whose output is in the interval [0, 1], then applied the same rounding transformation to obtain
scores in the desired range. We highlight the ensemble of Mobile-BERT and Electra because
on their own, this ensembled model provides a big increase in performance over BERT with
approximately the same computational footprint on its own.
6. Discussion
The goal of this paper was not to achieve state-of-the-art, but rather to show that we can
achieve significant results within a very modest memory footprint and computational budget.
We managed to exceed previous known results of BERT alone with approximately one third the
parameters. Combining these models with R2 variants of [47] or with features, as done in [40],
would be interesting since they do not add little to the computational load of the system.
There were noteworthy additions to the literature we did not consider for various reasons.
The Longformer of [4] uses a sliding attention window in which the resulting self-attention
mechanism scales linearly with length, however, the number of parameters of the pretrained
Longformer models often coincided or exceeded those of the BERT model. Like the Reformer
model of [21], the Linformer of [43], Sparse Transformer of [10], and Performer model of [9]
exploit the observation that the attention matrix (given by the softmax) can be approximated by
collection of low-rank matrices. Their different mechanisms for doing so means their complexity
scales differently. The Projection Attention Networks for Document Classification On-Device
(PRADO) model given by [24] seems promising, however, we did not have access to a version
of it we could use. The SHARNN of [29] also looks interesting, however, we found that the
architecture was difficult to use for transfer learning.
Transfer learning and language models improved the performance of document classification
texts in natural language processing domain. We should note that all these models are pertained
with a large corpus except for the Reformer model and we perform the fine-tuing process. Better
training could improve upon the results of the Reformer model.
There are several reasons these directions in research are important; as we are seeing more
and more attention given to power-efficient computing for usage on small-devices, we think the
deep learning community will see a greater emphasis on smaller efficient models in the future.
Secondly, this work provides a stepping stone to the classifying and evaluating documents where
the necessity for context to extend beyond the limits that current pretrained transformer-based
language models allow. Lastly we think combining the approaches that seem to have a beneficial
effect on performance should give smaller, better, and more environmentally friendly models in
the future.
9. AUTOMATED ESSAY SCORING USING EFFICIENT TRANSFORMER-BASED LANGUAGE MODELS 9
Acknowledgments
We would like to acknowledge Susan Lottridge and Balaji Kodeswaran for their support of
this project.
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CAMBIUM ASSESSMENT, INC.
Current address: 1000 Thomas Jefferson St., N.W. Washington, D.C. 20007
Email address: christopher.ormerod@cambiumassessment.com
UNIVERSITY OF COLORADO, BOULDER
Email address: Akanksha.Malhotra@colorado.edu
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