Harnessing Deep Neural Networks with Logic RulesSho Takase
This document summarizes a method for harnessing deep neural networks with logic rules. The goal is to incorporate general rules and human intuitions into neural networks. Rules are expressed using first-order predicate logic and incorporated into training as constraints. The method alternates between calculating the model distribution subject to constraints (q(y|x)) and updating the model parameters (θ). Experiments on sentiment analysis and named entity recognition show the approach improves performance by enforcing linguistic rules during training.
This document summarizes research on using structured event representations extracted from news articles to predict stock price movements. Key points include:
- Events are extracted from articles and represented as tuples of actors, actions, and objects to capture the who, what, when of events.
- A deep neural network model is used to predict stock price changes based on extracted event representations.
- The model achieves better performance than baselines that use bag-of-words representations of articles.
Phrase linguistic classification and generalization for improving statistical...Hiroshi Matsumoto
De Gispert, Adrià. "Phrase linguistic classification and generalization for improving statistical machine translation." Proceedings of the ACL Student Research Workshop. Association for Computational Linguistics, 2005.
Harnessing Deep Neural Networks with Logic RulesSho Takase
This document summarizes a method for harnessing deep neural networks with logic rules. The goal is to incorporate general rules and human intuitions into neural networks. Rules are expressed using first-order predicate logic and incorporated into training as constraints. The method alternates between calculating the model distribution subject to constraints (q(y|x)) and updating the model parameters (θ). Experiments on sentiment analysis and named entity recognition show the approach improves performance by enforcing linguistic rules during training.
This document summarizes research on using structured event representations extracted from news articles to predict stock price movements. Key points include:
- Events are extracted from articles and represented as tuples of actors, actions, and objects to capture the who, what, when of events.
- A deep neural network model is used to predict stock price changes based on extracted event representations.
- The model achieves better performance than baselines that use bag-of-words representations of articles.
Phrase linguistic classification and generalization for improving statistical...Hiroshi Matsumoto
De Gispert, Adrià. "Phrase linguistic classification and generalization for improving statistical machine translation." Proceedings of the ACL Student Research Workshop. Association for Computational Linguistics, 2005.
This document discusses a study that integrated multiple rule-based machine translation engines into a hybrid system using Moses. The system architecture combines the phrase tables from Moses and each RBMT system. The RBMT outputs are aligned and their phrase tables concatenated with the Moses phrase table. The tuning process adjusts weights for the additional columns from the RBMT phrase tables. Results showed BLEU score improvements from combining rule-based and data-driven approaches into a hybrid machine translation system.
This document summarizes a paper that explores relearning a rule-based machine translation (RBMT) system using statistical methods. It compares the performance of the original SYSTRAN RBMT system, a relearnt statistical model of SYSTRAN called SYSTRAN Relearnt, and a baseline statistical model called SYSTRAN Relearnt-0. The models are trained without parallel corpora by using SYSTRAN translations. Evaluation shows SYSTRAN Relearnt achieves 5 BLEU points higher than the baseline by using a real English language model and tuning set. Error analysis of 100 sentences identifies common error types between the systems like missing words, extra words, and translation choices to discriminate the nature and training of
This paper proposes a method for example-based machine translation that combines syntactic transfer with statistical models. The method uses transfer rules to construct the target language syntactic tree structure from the source language. It then uses a statistical generation module to select the best word sequence based on language and translation models. The method is evaluated on a travel domain corpus, with the combined approach outperforming a baseline of example-based transfer alone in terms of BLEU, NIST and human evaluation.
The document summarizes an English-Japanese example-based machine translation system developed by Microsoft Research (MSR-MT) that uses abstract linguistic representations. MSR-MT combines rule-based and statistical techniques with example-based transfer. It first parses sentence pairs into logical forms (LFs) and then extracts mappings between the LFs to create a bilingual knowledge base. New sentences are translated by matching their LFs to the knowledge base. An evaluation found MSR-MT performed comparably to a commercial system on a technical domain, suggesting example-based MT can achieve good results using semantic representations and alignment rules.
The document summarizes the BLEU method for automatically evaluating machine translation systems. BLEU calculates n-gram precision between a candidate translation and multiple reference translations, with modifications to address weaknesses. It combines the average logarithm of modified n-gram precisions with a brevity penalty for translations longer than references. Evaluation tests on multiple translation systems found BLEU scores reliably distinguished system quality and correlated well with human judgements.
This document describes a statistical approach to machine translation. It discusses using probability to determine the most likely source sentence S given a target sentence T. It presents methods for computing language model probabilities, translation probabilities, and searching for the optimal S. Two pilot experiments are described to estimate parameters for the translation model from bilingual text data. Evaluation of the second experiment showed the decoded sentences were either exact, alternate, different, wrong or ungrammatical compared to the reference translations.
Approach to japanese english automatic translation by Susumu KunoHiroshi Matsumoto
1. The document describes a machine translation system for translating Japanese text to another language.
2. It involves automatic input editing, segmentation, syntactical analysis, and output editing with transformation.
3. The system handles characteristics of Japanese text like having no spaces between words and using kanji characters, by segmenting the text into components and replacing kanji with word tokens.
This document discusses a study that integrated multiple rule-based machine translation engines into a hybrid system using Moses. The system architecture combines the phrase tables from Moses and each RBMT system. The RBMT outputs are aligned and their phrase tables concatenated with the Moses phrase table. The tuning process adjusts weights for the additional columns from the RBMT phrase tables. Results showed BLEU score improvements from combining rule-based and data-driven approaches into a hybrid machine translation system.
This document summarizes a paper that explores relearning a rule-based machine translation (RBMT) system using statistical methods. It compares the performance of the original SYSTRAN RBMT system, a relearnt statistical model of SYSTRAN called SYSTRAN Relearnt, and a baseline statistical model called SYSTRAN Relearnt-0. The models are trained without parallel corpora by using SYSTRAN translations. Evaluation shows SYSTRAN Relearnt achieves 5 BLEU points higher than the baseline by using a real English language model and tuning set. Error analysis of 100 sentences identifies common error types between the systems like missing words, extra words, and translation choices to discriminate the nature and training of
This paper proposes a method for example-based machine translation that combines syntactic transfer with statistical models. The method uses transfer rules to construct the target language syntactic tree structure from the source language. It then uses a statistical generation module to select the best word sequence based on language and translation models. The method is evaluated on a travel domain corpus, with the combined approach outperforming a baseline of example-based transfer alone in terms of BLEU, NIST and human evaluation.
The document summarizes an English-Japanese example-based machine translation system developed by Microsoft Research (MSR-MT) that uses abstract linguistic representations. MSR-MT combines rule-based and statistical techniques with example-based transfer. It first parses sentence pairs into logical forms (LFs) and then extracts mappings between the LFs to create a bilingual knowledge base. New sentences are translated by matching their LFs to the knowledge base. An evaluation found MSR-MT performed comparably to a commercial system on a technical domain, suggesting example-based MT can achieve good results using semantic representations and alignment rules.
The document summarizes the BLEU method for automatically evaluating machine translation systems. BLEU calculates n-gram precision between a candidate translation and multiple reference translations, with modifications to address weaknesses. It combines the average logarithm of modified n-gram precisions with a brevity penalty for translations longer than references. Evaluation tests on multiple translation systems found BLEU scores reliably distinguished system quality and correlated well with human judgements.
This document describes a statistical approach to machine translation. It discusses using probability to determine the most likely source sentence S given a target sentence T. It presents methods for computing language model probabilities, translation probabilities, and searching for the optimal S. Two pilot experiments are described to estimate parameters for the translation model from bilingual text data. Evaluation of the second experiment showed the decoded sentences were either exact, alternate, different, wrong or ungrammatical compared to the reference translations.
Approach to japanese english automatic translation by Susumu KunoHiroshi Matsumoto
1. The document describes a machine translation system for translating Japanese text to another language.
2. It involves automatic input editing, segmentation, syntactical analysis, and output editing with transformation.
3. The system handles characteristics of Japanese text like having no spaces between words and using kanji characters, by segmenting the text into components and replacing kanji with word tokens.