| Encoder-decoder models can benefit from pre-trained masked language models in grammatical error correction M Kaneko, M Mita, S Kiyono, J Suzuki, K Inui arXiv preprint arXiv:2005.00987, 2020 | 168 | 2020 |
| Gender-preserving debiasing for pre-trained word embeddings M Kaneko, D Bollegala arXiv preprint arXiv:1906.00742, 2019 | 167 | 2019 |
| Debiasing pre-trained contextualised embeddings M Kaneko, D Bollegala arXiv preprint arXiv:2101.09523, 2021 | 149 | 2021 |
| Unmasking the mask–evaluating social biases in masked language models M Kaneko, D Bollegala Proceedings of the AAAI conference on artificial intelligence 36 (11), 11954 …, 2022 | 79 | 2022 |
| Outfox: Llm-generated essay detection through in-context learning with adversarially generated examples R Koike, M Kaneko, N Okazaki Proceedings of the AAAI Conference on Artificial Intelligence 38 (19), 21258 …, 2024 | 70 | 2024 |
| Gender bias in masked language models for multiple languages M Kaneko, A Imankulova, D Bollegala, N Okazaki arXiv preprint arXiv:2205.00551, 2022 | 64 | 2022 |
| Debiasing isn't enough!--On the Effectiveness of Debiasing MLMs and their Social Biases in Downstream Tasks M Kaneko, D Bollegala, N Okazaki arXiv preprint arXiv:2210.02938, 2022 | 46 | 2022 |
| Exploring effectiveness of GPT-3 in grammatical error correction: A study on performance and controllability in prompt-based methods M Loem, M Kaneko, S Takase, N Okazaki arXiv preprint arXiv:2305.18156, 2023 | 44 | 2023 |
| Multi-head multi-layer attention to deep language representations for grammatical error detection M Kaneko, M Komachi Computación y Sistemas 23 (3), 883-891, 2019 | 44 | 2019 |
| Dictionary-based debiasing of pre-trained word embeddings M Kaneko, D Bollegala arXiv preprint arXiv:2101.09525, 2021 | 43 | 2021 |
| Grammatical Error Detection Using Error-and Grammaticality-Specific Word Embeddings M Kaneko, Y Sakaizawa, M Komachi Proceedings of the Eighth International Joint Conference on Natural Language …, 2017 | 41 | 2017 |
| Interpretability for language learners using example-based grammatical error correction M Kaneko, S Takase, A Niwa, N Okazaki arXiv preprint arXiv:2203.07085, 2022 | 35 | 2022 |
| In-Contextual Gender Bias Suppression for Large Language Models D Oba, M Kaneko, D Bollegala arXiv preprint arXiv:2309.07251, 2023 | 34 | 2023 |
| Evaluating gender bias in large language models via chain-of-thought prompting M Kaneko, D Bollegala, N Okazaki, T Baldwin arXiv preprint arXiv:2401.15585, 2024 | 28 | 2024 |
| Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models---Is Single-Corpus Evaluation Enough? M Mita, T Mizumoto, M Kaneko, R Nagata, K Inui arXiv preprint arXiv:1904.02927, 2019 | 28 | 2019 |
| Sense Embeddings are also Biased--Evaluating Social Biases in Static and Contextualised Sense Embeddings Y Zhou, M Kaneko, D Bollegala arXiv preprint arXiv:2203.07523, 2022 | 27 | 2022 |
| SOME: Reference-less sub-metrics optimized for manual evaluations of grammatical error correction R Yoshimura, M Kaneko, T Kajiwara, M Komachi Proceedings of the 28th International Conference on Computational …, 2020 | 27 | 2020 |
| TMU transformer system using BERT for re-ranking at BEA 2019 grammatical error correction on restricted track M Kaneko, K Hotate, S Katsumata, M Komachi Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building …, 2019 | 23 | 2019 |
| Sentence concatenation approach to data augmentation for neural machine translation S Kondo, K Hotate, M Kaneko, M Komachi arXiv preprint arXiv:2104.08478, 2021 | 20 | 2021 |
| Autoencoding improves pre-trained word embeddings M Kaneko, D Bollegala arXiv preprint arXiv:2010.13094, 2020 | 18 | 2020 |
Danushka BollegalaProfessor, The University of Liverpool, DMML, Amazon.comZweryfikowany adres z amazon.com