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Hitomi Yanaka
Hitomi Yanaka
Verified email at is.s.u-tokyo.ac.jp - Homepage
Title
Cited by
Cited by
Year
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
432019
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
312019
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
222020
Acquisition of phrase correspondences using natural deduction proofs
H Yanaka, K Mineshima, P Martínez-Gómez, D Bekki
arXiv preprint arXiv:1804.07656, 2018
132018
Exploring transitivity in neural NLI models through veridicality
H Yanaka, K Mineshima, K Inui
arXiv preprint arXiv:2101.10713, 2021
92021
Determining semantic textual similarity using natural deduction proofs
H Yanaka, K Mineshima, P Martínez-Gómez, D Bekki
arXiv preprint arXiv:1707.08713, 2017
82017
Multimodal logical inference system for visual-textual entailment
R Suzuki, H Yanaka, M Yoshikawa, K Mineshima, D Bekki
arXiv preprint arXiv:1906.03952, 2019
72019
SyGNS: A systematic generalization testbed based on natural language semantics
H Yanaka, K Mineshima, K Inui
arXiv preprint arXiv:2106.01077, 2021
42021
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
22021
Do Grammatical Error Correction Models Realize Grammatical Generalization?
M Mita, H Yanaka
arXiv preprint arXiv:2106.03031, 2021
22021
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
22018
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
22016
Compositional Evaluation on Japanese Textual Entailment and Similarity
H Yanaka, K Mineshima
arXiv preprint arXiv:2208.04826, 2022
2022
Annotating Japanese Numeral Expressions for a Logical and Pragmatic Inference Dataset
K Koyano, H Yanaka, K Mineshima, D Bekki
Testing the Annotation Consistency of Hallidayan Transitivity Processes A …, 2022
2022
Compositional Semantics and Inference System for Temporal Order based on Japanese CCG
T Sugimoto, H Yanaka
arXiv preprint arXiv:2204.09245, 2022
2022
Logical Inference for Counting on Semi-structured Tables
T Kurosawa, H Yanaka
arXiv preprint arXiv:2204.07803, 2022
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
2021
Learning Semantic Textual Relatedness using Natural Deduction Proofs
H YANAKA, K MINESHIMA, P MARTINEZ-GOMEZ, D BEKKI
自然言語処理 25 (3), 295-324, 2018
2018
Clustering Documents Using Structural Similarity Based on Case Sets-Applied for Technological Problems from Patents
H Yanaka, Y Ohsawa
2nd European Workshop on Chance Discovery and Data Synthesis (EWCDDS16), 1, 2016
2016
Issue Tracking System Using Data Jacket in the Same Business
H Yanaka, Y Ohsawa
IEICE Technical Report; IEICE Tech. Rep. 115 (337), 23-27, 2015
2015
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