| Can neural networks understand monotonicity reasoning? H Yanaka, K Mineshima, D Bekki, K Inui, S Sekine, L Abzianidze, J Bos arXiv preprint arXiv:1906.06448, 2019 | 72 | 2019 |
| HELP: A dataset for identifying shortcomings of neural models in monotonicity reasoning H Yanaka, K Mineshima, D Bekki, K Inui, S Sekine, L Abzianidze, J Bos arXiv preprint arXiv:1904.12166, 2019 | 53 | 2019 |
| Do neural models learn systematicity of monotonicity inference in natural language? H Yanaka, K Mineshima, D Bekki, K Inui arXiv preprint arXiv:2004.14839, 2020 | 43 | 2020 |
| Acquisition of phrase correspondences using natural deduction proofs H Yanaka, K Mineshima, P Martínez-Gómez, D Bekki arXiv preprint arXiv:1804.07656, 2018 | 24 | 2018 |
| Exploring transitivity in neural NLI models through veridicality H Yanaka, K Mineshima, K Inui arXiv preprint arXiv:2101.10713, 2021 | 17 | 2021 |
| Do grammatical error correction models realize grammatical generalization? M Mita, H Yanaka arXiv preprint arXiv:2106.03031, 2021 | 14 | 2021 |
| Multimodal logical inference system for visual-textual entailment R Suzuki, H Yanaka, M Yoshikawa, K Mineshima, D Bekki arXiv preprint arXiv:1906.03952, 2019 | 14 | 2019 |
| SyGNS: A systematic generalization testbed based on natural language semantics H Yanaka, K Mineshima, K Inui arXiv preprint arXiv:2106.01077, 2021 | 11 | 2021 |
| Compositional Evaluation on Japanese Textual Entailment and Similarity H Yanaka, K Mineshima Transactions of the Association for Computational Linguistics 10, 1266-1284, 2022 | 9 | 2022 |
| Determining semantic textual similarity using natural deduction proofs H Yanaka, K Mineshima, P Martínez-Gómez, D Bekki arXiv preprint arXiv:1707.08713, 2017 | 8 | 2017 |
| Assessing the generalization capacity of pre-trained language models through Japanese adversarial natural language inference H Yanaka, K Mineshima Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting …, 2021 | 7 | 2021 |
| Neural sentence generation from formal semantics K Manome, M Yoshikawa, H Yanaka, P Martínez-Gómez, K Mineshima, ... Proceedings of the 11th International Conference on Natural Language …, 2018 | 4 | 2018 |
| Compositional Semantics and Inference System for Temporal Order based on Japanese CCG T Sugimoto, H Yanaka arXiv preprint arXiv:2204.09245, 2022 | 3 | 2022 |
| Clustering documents on case vectors represented by predicate-argument structures-applied for eliciting technological problems from patents H Yanaka, Y Ohsawa 2016 Federated Conference on Computer Science and Information Systems …, 2016 | 3 | 2016 |
| Logical Inference for Counting on Semi-structured Tables T Kurosawa, H Yanaka arXiv preprint arXiv:2204.07803, 2022 | 2 | 2022 |
| Is Japanese CCGBank empirically correct? A case study of passive and causative constructions D Bekki, H Yanaka arXiv preprint arXiv:2302.14708, 2023 | 1 | 2023 |
| Annotating Japanese Numeral Expressions for a Logical and Pragmatic Inference Dataset K Koyano, H Yanaka, K Mineshima, D Bekki Proceedings of the 18th Joint ACL-ISO Workshop on Interoperable Semantic …, 2022 | 1 | 2022 |
| Building a video-and-language dataset with human actions for multimodal logical inference R Suzuki, H Yanaka, K Mineshima, D Bekki arXiv preprint arXiv:2106.14137, 2021 | 1 | 2021 |
| [TACL] Compositional Evaluation on Japanese Textual Entailment and Similarity H Yanaka, K Mineshima The 61st Annual Meeting Of The Association For Computational Linguistics, 2023 | | 2023 |
| Knowledge Injection for Disease Names in Logical Inference between Japanese Clinical Texts N Murakami, M Ishida, Y Takahashi, H Yanaka, D Bekki Proceedings of the 5th Clinical Natural Language Processing Workshop, 108-117, 2023 | | 2023 |
Koji MineshimaKeio UniversityVerified email at abelard.flet.keio.ac.jp
