Vasileios Lampos - 2014 - Predicting and Characterising User Impact on Twitter

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Association for Computational Linguistics

This document discusses predicting and characterizing user impact on Twitter. It uses features about users' tweets, profiles, and discussed topics to predict how influential a user is through regression and non-linear methods. Bubble size on a graph is inversely proportional to learned kernel lengths and color splits between feature types.

This paper contributes a noun phrase-annotated SMS corpus and proposes a weak semi-Markov CRF model for noun phrase chunking in informal text. The weak semi-CRF model improves training speed over linear-CRF and semi-CRF models while maintaining similar accuracy. Experiments on the SMS corpus show the weak semi-CRF achieves F1 scores comparable to other models but trains faster, especially with larger training data sizes.

Castro - 2018 - A High Coverage Method for Automatic False Friends Detection ...

Association for Computational Linguistics

This document presents a new method for automatically detecting false friends between Spanish and Portuguese using word embeddings. The method builds word vector spaces for each language using word2vec, finds a linear transformation between the spaces, and measures vector distances to classify word pairs as cognates or false friends. In experiments on a dataset of 710 word pairs, the method achieved state-of-the-art accuracy of 77.28% and high coverage of 97.91%, outperforming previous work. Future work will explore using different word embeddings and fine-grained classifications of partial false friends.

Castro - 2018 - A Crowd-Annotated Spanish Corpus for Humour Analysis

Association for Computational Linguistics

This document describes a Spanish language corpus for humor analysis that was created through crowd-sourcing annotations. Over 27,000 tweets were collected from humorous accounts and annotated through a web interface. The corpus contains over 100,000 annotations of the tweets' humor and funniness. Inter-annotator agreement was higher for this corpus than a previous Spanish humor corpus. The dataset will help analyze subjectivity in humor and was used in a shared task on humor classification and funniness prediction.

Muthu Kumar Chandrasekaran - 2018 - Countering Position Bias in Instructor In...

Association for Computational Linguistics

This document discusses position bias in instructor interventions in MOOC discussion forums. It finds that instructors are more likely to intervene in threads that appear higher on the discussion forum user interface due to their recent activity. To address this, it proposes a debiased classifier that weights examples based on their propensity for intervention. It finds this approach identifies intervention opportunities that were overlooked due to position bias. The debiased classifier outperforms a standard classifier on several metrics, demonstrating it can better predict unbiased intervention needs.

Daniel Gildea - 2018 - The ACL Anthology: Current State and Future Directions

Association for Computational Linguistics

The document summarizes the history and current state of the ACL Anthology, a repository of publications from ACL-sponsored conferences. It discusses how the Anthology was established in 2001 and is now maintained by volunteers, containing over 45,000 papers. The presentation calls for community involvement to help future-proof the Anthology through efforts like migrating its infrastructure and improving documentation. It also proposes hosting the Anthology on the main ACL website and recruiting a new editor.

Elior Sulem - 2018 - Semantic Structural Evaluation for Text Simplification

Association for Computational Linguistics

The document presents SAMSA, a new automatic evaluation measure for structural text simplification. SAMSA uses semantic parsing to measure the preservation of semantic structures and relations between an original text and its simplified version. It correlates significantly better with human judgments of meaning preservation and structural simplicity than prior reference-based metrics. SAMSA is the first evaluation method designed specifically for structural simplification operations like sentence splitting.

Daniel Gildea - 2018 - The ACL Anthology: Current State and Future Directions

Association for Computational Linguistics

Wenqiang Lei - 2018 - Sequicity: Simplifying Task-oriented Dialogue Systems w...

Association for Computational Linguistics

(1) Sequicity is a framework that simplifies task-oriented dialogue systems using single sequence-to-sequence architectures. (2) It formalizes dialogues as sequences of belief spans and responses and decodes them in two stages: generating a belief span followed by a response. (3) An experiment on two datasets found that a two-stage CopyNet instantiation of Sequicity outperformed several baselines in effectiveness, efficiency and handling out-of-vocabulary requests.

The document summarizes a study that explored how people's strategies for giving commands to a robot change over time during a collaborative navigation task. Ten participants each directed a robot for one hour via dialogue. Initially, participants predominantly used metric units like distances in their commands, but over time their commands increasingly referred to environmental landmarks. The study collected audio, text, and robot data to analyze parameters in commands. Future work aims to automate dialogue response generation based on this data.

Venkatesh Duppada - 2017 - SeerNet at EmoInt-2017: Tweet Emotion Intensity Es...

Association for Computational Linguistics

The document describes a system for estimating emotion intensity in tweets. It takes a lexicon-based and word vector-based approach to create sentence embeddings for tweets. Various regression models are trained and an ensemble is used to predict emotion intensity scores between 0-1 for anger, sadness, joy and fear. The system achieved third place in predicting emotion intensity and second place for intensities over 0.5. Future work involves using contextual sentence embeddings to improve predictions.

Satoshi Sonoh - 2015 - Toshiba MT System Description for the WAT2015 Workshop

Association for Computational Linguistics

This document describes Toshiba's machine translation system submitted to the WAT2015 workshop. It discusses using statistical post-editing (SPE) to improve rule-based machine translation (RBMT) output, as well as combining SPE and SMT systems using reranking with recurrent neural network language models. Experimental results show that the combined system achieved the best BLEU and RIBES scores compared to the individual SPE and SMT systems on several language pairs, including Japanese-English and Chinese-Japanese. However, human evaluation correlations were not entirely clear.

Chenchen Ding - 2015 - NICT at WAT 2015

Association for Computational Linguistics

John Richardson - 2015 - KyotoEBMT System Description for the 2nd Workshop on...

Association for Computational Linguistics

The document describes improvements made to the KyotoEBMT machine translation system. It discusses using forest parsing of input sentences to handle parsing errors and syntactic divergences. It also describes using the Nile alignment tool along with constituent parsing to improve word alignments from the training corpus. New features were added and the reranking was improved by incorporating a neural machine translation-based bilingual language model.

John Richardson - 2015 - KyotoEBMT System Description for the 2nd Workshop on...

Association for Computational Linguistics

El documento describe el sistema de traducción basado en ejemplos KyotoEBMT. El sistema utiliza análisis de dependencia tanto del idioma origen como del idioma destino y puede manejar ambigüedades en las hipótesis de traducción mediante el uso de reglas de rejilla. Los resultados oficiales del WAT2015 muestran mejoras en las métricas BLEU y RIBES con la reranqueación de traducciones, aunque la reranqueación empeora la evaluación humana para la dirección de traducción japonés-chino. El sistema Ky

Zhongyuan Zhu - 2015 - Evaluating Neural Machine Translation in English-Japan...

Association for Computational Linguistics

This document evaluates several neural machine translation models for English to Japanese translation. It finds that simple neural models outperform statistical machine translation baselines. Soft attention models with LSTM units performed best. However, training these models on pre-reordered data hurt performance. The neural models tended to produce grammatically correct but incomplete translations by omitting information. Replacing unknown words helped some models but more sophisticated solutions are needed for models trained on natural order data.

Zhongyuan Zhu - 2015 - Evaluating Neural Machine Translation in English-Japan...

Association for Computational Linguistics

This document evaluates various neural machine translation models for English to Japanese translation. It compares different network architectures, recurrent units, and training data configurations. Results show that soft-attention models outperformed multi-layer encoder-decoder models, and training on pre-reordered data hurt performance. Neural machine translation models tended to generate grammatically correct but incomplete translations.

Hyoung-Gyu Lee - 2015 - NAVER Machine Translation System for WAT 2015

Association for Computational Linguistics

This document describes NAVER's machine translation systems for the WAT 2015 evaluation. For English-to-Japanese translation, the best system combined tree-to-string syntax-based machine translation with neural machine translation re-ranking, achieving a BLEU score of 34.60. For Korean-to-Japanese translation, the top system used phrase-based machine translation and neural machine translation re-ranking, obtaining a BLEU score of 71.38. The document also analyzes the effectiveness of character-level tokenization and other techniques for neural machine translation.

Satoshi Sonoh - 2015 - Toshiba MT System Description for the WAT2015 Workshop

Association for Computational Linguistics

Toshiba presented their machine translation system for the WAT2015 workshop. Their system uses statistical post-editing (SPE) to correct rule-based machine translation (RBMT) output. It also combines SPE and phrase-based statistical machine translation (SMT) results by reranking the merged n-best lists using a recurrent neural network language model. Evaluation showed the combined system achieved the best results on most language pairs compared to SPE and SMT individually. Analysis of system selections by the combination found it primarily chose translations from SPE.

Chenchen Ding - 2015 - NICT at WAT 2015

Association for Computational Linguistics

Graham Neubig - 2015 - Neural Reranking Improves Subjective Quality of Machin...

Association for Computational Linguistics

Neural reranking of machine translation output improves both automatic metrics and subjective human evaluations of translation quality. The document analyzes reranking results from a statistical machine translation system using an attentional neural machine translation model. Reranking corrected errors related to reordering, insertion, deletion, substitution and conjugation. Specifically, it improved phrasal reordering, auxiliary verb insertion/deletion, and coordinate structures. The gains were mainly in grammatical aspects rather than lexical selection. While reranking is shown to be effective, questions remain about comparing it to pure neural machine translation and neural language models.

Graham Neubig - 2015 - Neural Reranking Improves Subjective Quality of Machin...

Association for Computational Linguistics

This document discusses using neural reranking to improve the subjective quality of machine translation. It finds that reranking N-best lists generated by a baseline machine translation system using neural models leads to improvements in both automatic metrics like BLEU and manual evaluations of translation quality. A qualitative analysis shows that reranking most improves reordering, insertion, and conjugation errors while having less success with terminology. The analysis suggests neural reranking is an effective technique for machine translation enhancement.

Terumasa Ehara - 2015 - System Combination of RBMT plus SPE and Preordering p...

Association for Computational Linguistics

Terumasa Ehara - 2015 - System Combination of RBMT plus SPE and Preordering p...

Association for Computational Linguistics

This document describes a hybrid machine translation system that combines rule-based and statistical approaches. The system architecture includes a rule-based machine translation (RBMT) component, statistical phrase-based translation (SPE), preordering, and statistical machine translation (SMT). Improvements are made to the Chinese parsing grammar and k-best parse tree reranking. Experimental results on several language pairs show the hybrid system achieves better BLEU and RIBES scores than the individual RBMT or SMT systems. Further work is needed to improve parsing accuracy.

Toshiaki Nakazawa - 2015 - Overview of the 2nd Workshop on Asian Translation

Association for Computational Linguistics

This document provides an overview of the WAT2015 machine translation evaluation competition. It includes a table listing the participating teams and their organizational affiliations. It also includes a graph comparing the BLEU and crowd-sourced scores between the WAT2014 and WAT2015 competitions, noting that the WAT2015 crowd scores used the first three judgments to match the WAT2014 evaluation criteria.

Hua Shan - 2015 - A Dependency-to-String Model for Chinese-Japanese SMT System

Association for Computational Linguistics

1) The document presents a dependency-to-string translation model for a Chinese-Japanese statistical machine translation system. 2) The system achieves a BLEU score of 34.87 and a RIBES score of 79.25 on the Chinese-Japanese translation task, outperforming a baseline PBSMT system. 3) The dependency-to-string model uses two types of translation rules - HDR rules with generalized dependency fragments on the source side and strings on the target side, and H rules with single words on the source side.

Wei Yang - 2015 - Sampling-based Alignment and Hierarchical Sub-sentential Al...

Association for Computational Linguistics

This paper evaluates different alignment methods for Chinese to Japanese patent translation, including sampling-based alignment and hierarchical sub-sentential alignment. Experimental results show this combined method significantly reduces training time compared to traditional GIZA++ alignment, with translation quality remaining steady. Specifically, using this method, training time was reduced to just 57 minutes while maintaining comparable BLEU scores, representing a five-fold decrease compared to GIZA++. The paper concludes this approach can effectively accelerate statistical machine translation system development for patent translation tasks.

Wei Yang - 2015 - Sampling-based Alignment and Hierarchical Sub-sentential Al...

Association for Computational Linguistics

The document reports on experiments comparing different alignment methods for Chinese to Japanese statistical machine translation of patents. It shows results from using the GIZA++ aligner versus the MGIZA aligner on a dataset, with MGIZA achieving a slightly higher BLEU score. It also shows results from combining sampling-based alignment with hierarchical sub-sentential alignment, achieving the highest BLEU score but with longer training times. The document is from a presentation on this work.

Katsuhito Sudoh - 2015 Chinese-to-Japanese Patent Machine Translation based o...

Association for Computational Linguistics

The document summarizes research on Chinese-to-Japanese patent machine translation using syntactic pre-ordering. It presents two pre-ordering approaches: rule-based pre-ordering that reorders Chinese sentences to head-final order based on dependency parsing; and data-driven pre-ordering that learns to rank word order using machine learning. Evaluation shows the rule-based system achieves comparable results to the baseline translation system, while being more efficient, though with some loss of accuracy compared to the data-driven approach. Pre-ordering is found to be effective but challenges remain in word alignment and domain adaptation.

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