Papers by Masashi Yoshikawa
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. |
Empirical Investigation of Neural Symbolic Reasoning Strategies (2023.findings-eacl)
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Yoichi Aoki, Keito Kudo, Tatsuki Kuribayashi, Ana Brassard, Masashi Yoshikawa, Keisuke Sakaguchi, Kentaro Inui
| Challenge: | Neural reasoning accuracy improves when generating intermediate reasoning steps. |
| Approach: | They decompose the reasoning strategy w.r.t. step granularity and chaining strategy. |
| Outcome: | The proposed reasoning strategy significantly affects performance in a symbolic reasoning dataset. |
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. |
Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning? (2023.eacl-main)
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Keito Kudo, Yoichi Aoki, Tatsuki Kuribayashi, Ana Brassard, Masashi Yoshikawa, Keisuke Sakaguchi, Kentaro Inui
| Challenge: | Using a pre-trained dataset, we examine how well recent neural models capture compositionality in symbolic reasoning tasks. |
| Approach: | They propose a skill tree on compositionality that defines hierarchical levels of complexity along with three compositionality dimensions: systematicity, productivity, and substitutivity. |
| Outcome: | The proposed model struggled most with systematicity, performing poorly even with relatively simple compositions. |
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. |
Instance-Based Neural Dependency Parsing (2021.tacl-1)
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Hiroki Ouchi, Jun Suzuki, Sosuke Kobayashi, Sho Yokoi, Tatsuki Kuribayashi, Masashi Yoshikawa, Kentaro Inui
| Challenge: | Existing models that use instance-based inference for dependency parsing are difficult to understand for humans. |
| Approach: | They develop neural models that adopt an interpretable inference process for dependency parsing. |
| Outcome: | The proposed models achieve competitive accuracy with standard neural models and have plausibility of instance-based explanations. |