Pascual Martínez-Gómez

Also published as: Pascual Martínez Gómez


2018

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Acquisition of Phrase Correspondences Using Natural Deduction Proofs
Hitomi Yanaka | Koji Mineshima | Pascual Martínez-Gómez | Daisuke Bekki
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

How to identify, extract, and use phrasal knowledge is a crucial problem for the task of Recognizing Textual Entailment (RTE). To solve this problem, we propose a method for detecting paraphrases via natural deduction proofs of semantic relations between sentence pairs. Our solution relies on a graph reformulation of partial variable unifications and an algorithm that induces subgraph alignments between meaning representations. Experiments show that our method can automatically detect various paraphrases that are absent from existing paraphrase databases. In addition, the detection of paraphrases using proof information improves the accuracy of RTE tasks.

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Neural sentence generation from formal semantics
Kana Manome | Masashi Yoshikawa | Hitomi Yanaka | Pascual Martínez-Gómez | Koji Mineshima | Daisuke Bekki
Proceedings of the 11th International Conference on Natural Language Generation

Sequence-to-sequence models have shown strong performance in a wide range of NLP tasks, yet their applications to sentence generation from logical representations are underdeveloped. In this paper, we present a sequence-to-sequence model for generating sentences from logical meaning representations based on event semantics. We use a semantic parsing system based on Combinatory Categorial Grammar (CCG) to obtain data annotated with logical formulas. We augment our sequence-to-sequence model with masking for predicates to constrain output sentences. We also propose a novel evaluation method for generation using Recognizing Textual Entailment (RTE). Combining parsing and generation, we test whether or not the output sentence entails the original text and vice versa. Experiments showed that our model outperformed a baseline with respect to both BLEU scores and accuracies in RTE.

2017

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Determining Semantic Textual Similarity using Natural Deduction Proofs
Hitomi Yanaka | Koji Mineshima | Pascual Martínez-Gómez | Daisuke Bekki
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Determining semantic textual similarity is a core research subject in natural language processing. Since vector-based models for sentence representation often use shallow information, capturing accurate semantics is difficult. By contrast, logical semantic representations capture deeper levels of sentence semantics, but their symbolic nature does not offer graded notions of textual similarity. We propose a method for determining semantic textual similarity by combining shallow features with features extracted from natural deduction proofs of bidirectional entailment relations between sentence pairs. For the natural deduction proofs, we use ccg2lambda, a higher-order automatic inference system, which converts Combinatory Categorial Grammar (CCG) derivation trees into semantic representations and conducts natural deduction proofs. Experiments show that our system was able to outperform other logic-based systems and that features derived from the proofs are effective for learning textual similarity.

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Visual Denotations for Recognizing Textual Entailment
Dan Han | Pascual Martínez-Gómez | Koji Mineshima
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

In the logic approach to Recognizing Textual Entailment, identifying phrase-to-phrase semantic relations is still an unsolved problem. Resources such as the Paraphrase Database offer limited coverage despite their large size whereas unsupervised distributional models of meaning often fail to recognize phrasal entailments. We propose to map phrases to their visual denotations and compare their meaning in terms of their images. We show that our approach is effective in the task of Recognizing Textual Entailment when combined with specific linguistic and logic features.

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The Challenge of Composition in Distributional and Formal Semantics
Ran Tian | Koji Mineshima | Pascual Martínez-Gómez
Proceedings of the IJCNLP 2017, Tutorial Abstracts

This is tutorial proposal. Abstract is as follows: The principle of compositionality states that the meaning of a complete sentence must be explained in terms of the meanings of its subsentential parts; in other words, each syntactic operation should have a corresponding semantic operation. In recent years, it has been increasingly evident that distributional and formal semantics are complementary in addressing composition; while the distributional/vector-based approach can naturally measure semantic similarity (Mitchell and Lapata, 2010), the formal/symbolic approach has a long tradition within logic-based semantic frameworks (Montague, 1974) and can readily be connected to theorem provers or databases to perform complicated tasks. In this tutorial, we will cover recent efforts in extending word vectors to account for composition and reasoning, the various challenging phenomena observed in composition and addressed by formal semantics, and a hybrid approach that combines merits of the two. Outline and introduction to instructors are found in the submission. Ran Tian has taught a tutorial at the Annual Meeting of the Association for Natural Language Processing in Japan, 2015. The estimated audience size was about one hundred. Only a limited part of the contents in this tutorial is drawn from the previous one. Koji Mineshima has taught a one-week course at the 28th European Summer School in Logic, Language and Information (ESSLLI2016), together with Prof. Daisuke Bekki. Only a few contents are the same with this tutorial. Tutorials on “CCG Semantic Parsing” have been given in ACL2013, EMNLP2014, and AAAI2015. A coming tutorial on “Deep Learning for Semantic Composition” will be given in ACL2017. Contents in these tutorials are somehow related to but not overlapping with our proposal.

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On-demand Injection of Lexical Knowledge for Recognising Textual Entailment
Pascual Martínez-Gómez | Koji Mineshima | Yusuke Miyao | Daisuke Bekki
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

We approach the recognition of textual entailment using logical semantic representations and a theorem prover. In this setup, lexical divergences that preserve semantic entailment between the source and target texts need to be explicitly stated. However, recognising subsentential semantic relations is not trivial. We address this problem by monitoring the proof of the theorem and detecting unprovable sub-goals that share predicate arguments with logical premises. If a linguistic relation exists, then an appropriate axiom is constructed on-demand and the theorem proving continues. Experiments show that this approach is effective and precise, producing a system that outperforms other logic-based systems and is competitive with state-of-the-art statistical methods.

2016

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Rule Extraction for Tree-to-Tree Transducers by Cost Minimization
Pascual Martínez-Gómez | Yusuke Miyao
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Building compositional semantics and higher-order inference system for a wide-coverage Japanese CCG parser
Koji Mineshima | Ribeka Tanaka | Pascual Martínez-Gómez | Yusuke Miyao | Daisuke Bekki
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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ccg2lambda: A Compositional Semantics System
Pascual Martínez-Gómez | Koji Mineshima | Yusuke Miyao | Daisuke Bekki
Proceedings of ACL-2016 System Demonstrations

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Paraphrase for Open Question Answering: New Dataset and Methods
Ying Xu | Pascual Martínez-Gómez | Yusuke Miyao | Randy Goebel
Proceedings of the Workshop on Human-Computer Question Answering

2015

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Distant-supervised Language Model for Detecting Emotional Upsurge on Twitter
Yoshinari Fujinuma | Hikaru Yokono | Pascual Martínez-Gómez | Akiko Aizawa
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation

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Higher-order logical inference with compositional semantics
Koji Mineshima | Pascual Martínez-Gómez | Yusuke Miyao | Daisuke Bekki
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2013

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Diagnosing Causes of Reading Difficulty using Bayesian Networks
Pascual Martínez-Gómez | Akiko Aizawa
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Effects of Parsing Errors on Pre-Reordering Performance for Chinese-to-Japanese SMT
Dan Han | Pascual Martínez-Gómez | Yusuke Miyao | Katsuhito Sudoh | Masaaki Nagata
Proceedings of the 27th Pacific Asia Conference on Language, Information, and Computation (PACLIC 27)

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Using unlabeled dependency parsing for pre-reordering for Chinese-to-Japanese statistical machine translation
Dan Han | Pascual Martínez-Gómez | Yusuke Miyao | Katsuhito Sudoh | Masaaki Nagata
Proceedings of the Second Workshop on Hybrid Approaches to Translation

2012

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Recognizing Personal Characteristics of Readers using Eye-Movements and Text Features
Pascual Martínez-Gómez | Tadayoshi Hara | Akiko Aizawa
Proceedings of COLING 2012

2010

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A Deterministic Annealing-Based Training Algorithm For Statistical Machine Translation Models
Pascual Martínez Gómez | Kei Hashimoto | Yoshihiko Nankaku | Keiichi Tokuda | Germán Sanchis-Trilles
Proceedings of the 14th Annual conference of the European Association for Machine Translation

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UPV-PRHLT English–Spanish System for WMT10
Germán Sanchis-Trilles | Jesús Andrés-Ferrer | Guillem Gascó | Jesús González-Rubio | Pascual Martínez-Gómez | Martha-Alicia Rocha | Joan-Andreu Sánchez | Francisco Casacuberta
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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The UPV-PRHLT Combination System for WMT 2010
Jesús González-Rubio | Germán Sanchis-Trilles | Joan-Andreu Sánchez | Jesús Andrés-Ferrer | Guillem Gascó | Pascual Martínez-Gómez | Martha-Alicia Rocha | Francisco Casacuberta
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR