Papers by Daisuke Bekki
Logical Inferences with Comparatives and Generalized Quantifiers (2020.acl-srw)
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| Challenge: | Comparative constructions pose a challenge in Natural Language Inference (NLI) Comparatives interact with quantifiers, numerals, and lexical antonyms, but a logical inference system for comparatives has not been developed for the task. |
| Approach: | They propose a compositional semantics system that maps comparative constructions to semantic representations via combinatory categorial grammar parsers and integrates it with an automated theorem proving system. |
| Outcome: | The proposed system outperforms previous logic-based systems and deep learning models on three NLI datasets. |
Acquisition of Phrase Correspondences Using Natural Deduction Proofs (N18-1)
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| Challenge: | Existing methods for Recognizing Textual Entailment (RTE) lack phrasal knowledge. |
| Approach: | They propose a method for detecting paraphrases via natural deduction proofs of semantic relations between sentence pairs. |
| Outcome: | The proposed method detects paraphrases that are absent from existing paraphrase databases and improves accuracy of RTE tasks. |
Multimodal Logical Inference System for Visual-Textual Entailment (P19-2)
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| Challenge: | Recent studies of multimodal inference provide challenging tasks such as visual question answering and visual reasoning. |
| Approach: | They propose an unsupervised multimodal logical inference system that can prove entailment relations between texts and images by combing semantic parsing and theorem proving. |
| Outcome: | The proposed system can handle semantically complex sentences for visual-textual inference. |
Reforging : A Method for Constructing a Linguistically Valid Japanese CCG Treebank (2024.eacl-srw)
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| Challenge: | Existing treebanks for Combinatory Categorial Grammar (CCG) are insufficient for linguistic validity of CCG . |
| Approach: | They propose to combine ABCTreebank and lightblue to generate a linguistically valid Japanese CCG treebank with detailed information by filtering lightblu's lexical items using ABCTtreebank. |
| Outcome: | The proposed method generates a linguistically valid Japanese CCG treebank with detailed information by combining the strengths of ABCTreebank and lightblue. |
Combining Event Semantics and Degree Semantics for Natural Language Inference (2020.coling-main)
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| Challenge: | formal semantics has been used to account for the semantics of verb phrases and adverbial modifiers . but it is not clear whether these independent theories can be combined and extended to cases in which the phenomena in question interact. |
| Approach: | They propose a logic-based NLI system that combines event semantics and degree semantics. |
| Outcome: | The proposed system achieves high accuracies on linguistically challenging datasets . the proposed system can handle various combinations of linguistic phenomena without compromise . |
Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language? (2020.acl-main)
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| Challenge: | Despite the success of language models using neural networks, it remains unclear to what extent neural models have the generalization ability to perform inferences. |
| Approach: | They propose a method to evaluate whether neural models can learn systematicity of monotonicity inference in natural language. |
| Outcome: | The proposed method shows that neural models can perform inferences on unseen combinations of lexical and logical phenomena when syntactic structures are similar between training and test sets. |
Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation (P19-1)
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| Challenge: | Existing methods for Combinatory Categorial Grammar (CCG) parsing are limited to a specific parser architecture, making it non-trivial to apply to current parsers. |
| Approach: | They propose a domain adaptation method for Combinatory Categorial Grammar (CCG) they propose to generate CCG corpora using cheaper dependency trees. |
| Outcome: | The proposed method improves on speech conversation and math problems. |
Consistent CCG Parsing over Multiple Sentences for Improved Logical Reasoning (N18-2)
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| Challenge: | Existing methods to recognize textual entailment use a CCG parser to process sentences . failing to recognize the similar syntactic structure results in inconsistent argument structures . |
| Approach: | They propose to extend existing CCG parsers to parse sentences consistently . they use an inter-sentence modeling with Markov Random Fields to achieve this . |
| Outcome: | The proposed method improves on English and Japanese languages. |