@inproceedings{yoshikawa-etal-2018-consistent,
title = "Consistent {CCG} Parsing over Multiple Sentences for Improved Logical Reasoning",
author = "Yoshikawa, Masashi and
Mineshima, Koji and
Noji, Hiroshi and
Bekki, Daisuke",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2065",
doi = "10.18653/v1/N18-2065",
pages = "407--412",
abstract = "In formal logic-based approaches to Recognizing Textual Entailment (RTE), a Combinatory Categorial Grammar (CCG) parser is used to parse input premises and hypotheses to obtain their logical formulas. Here, it is important that the parser processes the sentences consistently; failing to recognize the similar syntactic structure results in inconsistent predicate argument structures among them, in which case the succeeding theorem proving is doomed to failure. In this work, we present a simple method to extend an existing CCG parser to parse a set of sentences consistently, which is achieved with an inter-sentence modeling with Markov Random Fields (MRF). When combined with existing logic-based systems, our method always shows improvement in the RTE experiments on English and Japanese languages.",
}
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<abstract>In formal logic-based approaches to Recognizing Textual Entailment (RTE), a Combinatory Categorial Grammar (CCG) parser is used to parse input premises and hypotheses to obtain their logical formulas. Here, it is important that the parser processes the sentences consistently; failing to recognize the similar syntactic structure results in inconsistent predicate argument structures among them, in which case the succeeding theorem proving is doomed to failure. In this work, we present a simple method to extend an existing CCG parser to parse a set of sentences consistently, which is achieved with an inter-sentence modeling with Markov Random Fields (MRF). When combined with existing logic-based systems, our method always shows improvement in the RTE experiments on English and Japanese languages.</abstract>
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%0 Conference Proceedings
%T Consistent CCG Parsing over Multiple Sentences for Improved Logical Reasoning
%A Yoshikawa, Masashi
%A Mineshima, Koji
%A Noji, Hiroshi
%A Bekki, Daisuke
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F yoshikawa-etal-2018-consistent
%X In formal logic-based approaches to Recognizing Textual Entailment (RTE), a Combinatory Categorial Grammar (CCG) parser is used to parse input premises and hypotheses to obtain their logical formulas. Here, it is important that the parser processes the sentences consistently; failing to recognize the similar syntactic structure results in inconsistent predicate argument structures among them, in which case the succeeding theorem proving is doomed to failure. In this work, we present a simple method to extend an existing CCG parser to parse a set of sentences consistently, which is achieved with an inter-sentence modeling with Markov Random Fields (MRF). When combined with existing logic-based systems, our method always shows improvement in the RTE experiments on English and Japanese languages.
%R 10.18653/v1/N18-2065
%U https://aclanthology.org/N18-2065
%U https://doi.org/10.18653/v1/N18-2065
%P 407-412
Markdown (Informal)
[Consistent CCG Parsing over Multiple Sentences for Improved Logical Reasoning](https://aclanthology.org/N18-2065) (Yoshikawa et al., NAACL 2018)
ACL
- Masashi Yoshikawa, Koji Mineshima, Hiroshi Noji, and Daisuke Bekki. 2018. Consistent CCG Parsing over Multiple Sentences for Improved Logical Reasoning. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 407–412, New Orleans, Louisiana. Association for Computational Linguistics.