| Attention is Not Only a Weight: Analyzing Transformers with Vector Norms G Kobayashi, T Kuribayashi, S Yokoi, K Inui Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020 | 223 | 2020 |
| Word Rotator's Distance S Yokoi, R Takahashi, R Akama, J Suzuki, K Inui arXiv preprint arXiv:2004.15003, 2020 | 65 | 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 | 56 | 2020 |
| Evaluation of Similarity-based Explanations K Hanawa, S Yokoi, S Hara, K Inui The International Conference on Learning Representations, 2021 | 54 | 2021 |
| 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 | 35 | 2021 |
| Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness R Akama, S Yokoi, J Suzuki, K Inui arXiv preprint arXiv:2004.14008, 2020 | 20 | 2020 |
| Efficient Estimation of Influence of a Training Instance S Kobayashi, S Yokoi, J Suzuki, K Inui arXiv preprint arXiv:2012.04207, 2020 | 18 | 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 | 13* | 2023 |
| Unsupervised Learning of Style-sensitive Word Vectors R Akama, K Watanabe, S Yokoi, S Kobayashi, K Inui arXiv preprint arXiv:1805.05581, 2018 | 13 | 2018 |
| Unbalanced Optimal Transport for Unbalanced Word Alignment Y Arase, H Bao, S Yokoi arXiv preprint arXiv:2306.04116, 2023 | 9 | 2023 |
| 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 | 9 | 2020 |
| Norm of word embedding encodes information gain M Oyama, S Yokoi, H Shimodaira arXiv preprint arXiv:2212.09663, 2022 | 8 | 2022 |
| Why is sentence similarity benchmark not predictive of application-oriented task performance? K Abe, S Yokoi, T Kajiwara, K Inui Proceedings of the 3rd Workshop on Evaluation and Comparison of NLP Systems …, 2022 | 6 | 2022 |
| Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions S Yokoi, S Kobayashi, K Fukumizu, J Suzuki, K Inui Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018 | 6 | 2018 |
| Link Prediction in Sparse Networks by Incidence Matrix Factorization S Yokoi, H Kajino, H Kashima Journal of Information Processing 25, 477-485, 2017 | 6 | 2017 |
| Transformer Language Models Handle Word Frequency in Prediction Head G Kobayashi, T Kuribayashi, S Yokoi, K Inui arXiv preprint arXiv:2305.18294, 2023 | 5 | 2023 |
| 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 | 4 | 2021 |
| Learning Co-Substructures by Kernel Dependence Maximization S Yokoi, D Mochihashi, R Takahashi, N Okazaki, K Inui The 26th International Joint Conference on Artificial Intelligence (IJCAI …, 2017 | 3 | 2017 |
| Revisiting Additive Compositionality: AND, OR and NOT Operations with Word Embeddings M Naito, S Yokoi, G Kim, H Shimodaira arXiv preprint arXiv:2105.08585, 2021 | 2 | 2021 |
| Computationally efficient Wasserstein loss for structured labels A Toyokuni, S Yokoi, H Kashima, M Yamada arXiv preprint arXiv:2103.00899, 2021 | 2 | 2021 |
Kentaro InuiMBZUAI, Tohoku University, RIKENVerified email at mbzuai.ac.ae
