| Attention is not only a weight: Analyzing transformers with vector norms G Kobayashi, T Kuribayashi, S Yokoi, K Inui arXiv preprint arXiv:2004.10102, 2020 | 196 | 2020 |
| Instance-based learning of span representations: A case study through named entity recognition H Ouchi, J Suzuki, S Kobayashi, S Yokoi, T Kuribayashi, R Konno, K Inui arXiv preprint arXiv:2004.14514, 2020 | 46 | 2020 |
| Lower perplexity is not always human-like T Kuribayashi, Y Oseki, T Ito, R Yoshida, M Asahara, K Inui arXiv preprint arXiv:2106.01229, 2021 | 45 | 2021 |
| An empirical study of span representations in argumentation structure parsing T Kuribayashi, H Ouchi, N Inoue, P Reisert, T Miyoshi, J Suzuki, K Inui Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019 | 43 | 2019 |
| Incorporating residual and normalization layers into analysis of masked language models G Kobayashi, T Kuribayashi, S Yokoi, K Inui arXiv preprint arXiv:2109.07152, 2021 | 24 | 2021 |
| Feasible annotation scheme for capturing policy argument reasoning using argument templates P Reisert, N Inoue, T Kuribayashi, K Inui Proceedings of the 5th Workshop on Argument Mining, 79-89, 2018 | 22 | 2018 |
| Context limitations make neural language models more human-like T Kuribayashi, Y Oseki, A Brassard, K Inui arXiv preprint arXiv:2205.11463, 2022 | 19 | 2022 |
| Diamonds in the rough: Generating fluent sentences from early-stage drafts for academic writing assistance T Ito, T Kuribayashi, H Kobayashi, A Brassard, M Hagiwara, J Suzuki, ... arXiv preprint arXiv:1910.09180, 2019 | 19 | 2019 |
| Langsmith: An interactive academic text revision system T Ito, T Kuribayashi, M Hidaka, J Suzuki, K Inui arXiv preprint arXiv:2010.04332, 2020 | 12 | 2020 |
| Analyzing feed-forward blocks in transformers through the lens of attention map G Kobayashi, T Kuribayashi, S Yokoi, K Inui arXiv preprint arXiv:2302.00456, 2023 | 9* | 2023 |
| TEASPN: Framework and protocol for integrated writing assistance environments M Hagiwara, T Ito, T Kuribayashi, J Suzuki, K Inui arXiv preprint arXiv:1909.02621, 2019 | 9 | 2019 |
| Modeling Event Salience in Narratives via Barthes' Cardinal Functions T Otake, S Yokoi, N Inoue, R Takahashi, T Kuribayashi, K Inui arXiv preprint arXiv:2011.01785, 2020 | 8 | 2020 |
| Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning? K Kudo, Y Aoki, T Kuribayashi, A Brassard, M Yoshikawa, K Sakaguchi, ... arXiv preprint arXiv:2302.07866, 2023 | 4 | 2023 |
| Towards exploiting argumentative context for argumentative relation identification T Kuribayashi, P Reisert, N Inoue, K Inui Proceedings of the Annual Meeting of the Association for Natural Language …, 2018 | 4 | 2018 |
| Transformer language models handle word frequency in prediction head G Kobayashi, T Kuribayashi, S Yokoi, K Inui arXiv preprint arXiv:2305.18294, 2023 | 3 | 2023 |
| Topicalization in Language Models: A Case Study on Japanese R Fujihara, T Kuribayashi, K Abe, R Tokuhisa, K Inui Proceedings of the 29th International Conference on Computational …, 2022 | 3 | 2022 |
| Instance-based neural dependency parsing H Ouchi, J Suzuki, S Kobayashi, S Yokoi, T Kuribayashi, M Yoshikawa, ... Transactions of the Association for Computational Linguistics 9, 1493-1507, 2021 | 3 | 2021 |
| Language models as an alternative evaluator of word order hypotheses: A case study in Japanese T Kuribayashi, T Ito, J Suzuki, K Inui arXiv preprint arXiv:2005.00842, 2020 | 3 | 2020 |
| Examining macro-level argumentative structure features for argumentative relation identification T Kuribayashi, P Reisert, N Inoue, K Inui 電子情報通信学会技術研究報告= IEICE technical report: 信学技報 117 (367), 37-42, 2017 | 3 | 2017 |
| Psychometric Predictive Power of Large Language Models T Kuribayashi, Y Oseki, T Baldwin arXiv preprint arXiv:2311.07484, 2023 | 2 | 2023 |
Kentaro InuiMBZUAI and Tohoku UniversityVerified email at ecei.tohoku.ac.jp
