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Masahiro Kaneko
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Publications
- 2024
- [c135]Ryuto Koike, Masahiro Kaneko, Naoaki Okazaki:
OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated Examples. AAAI 2024: 21258-21266 - [c134]Masahiro Kaneko, Graham Neubig, Naoaki Okazaki:
Solving NLP Problems through Human-System Collaboration: A Discussion-based Approach. EACL (Findings) 2024: 1644-1658 - [c133]Daisuke Oba, Masahiro Kaneko, Danushka Bollegala:
In-Contextual Gender Bias Suppression for Large Language Models. EACL (Findings) 2024: 1722-1742 - [i37]Masahiro Kaneko, Danushka Bollegala, Timothy Baldwin:
The Gaps between Pre-train and Downstream Settings in Bias Evaluation and Debiasing. CoRR abs/2401.08511 (2024) - [i36]Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki, Timothy Baldwin:
Evaluating Gender Bias in Large Language Models via Chain-of-Thought Prompting. CoRR abs/2401.15585 (2024) - [i35]Masahiro Kaneko, Danushka Bollegala, Timothy Baldwin:
Eagle: Ethical Dataset Given from Real Interactions. CoRR abs/2402.14258 (2024) - [i34]Masanari Ohi, Masahiro Kaneko, Ryuto Koike, Mengsay Loem, Naoaki Okazaki:
Likelihood-based Mitigation of Evaluation Bias in Large Language Models. CoRR abs/2402.15987 (2024) - [i33]Masahiro Kaneko, Timothy Baldwin:
A Little Leak Will Sink a Great Ship: Survey of Transparency for Large Language Models from Start to Finish. CoRR abs/2403.16139 (2024) - 2023
- [c132]Mengsay Loem, Masahiro Kaneko, Sho Takase, Naoaki Okazaki:
Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods. BEA@ACL 2023: 205-219 - [c131]Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki:
Comparing Intrinsic Gender Bias Evaluation Measures without using Human Annotated Examples. EACL 2023: 2849-2855 - [c130]Masahiro Kaneko, Naoaki Okazaki:
Reducing Sequence Length by Predicting Edit Spans with Large Language Models. EMNLP 2023: 10017-10029 - [c129]Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki:
The Impact of Debiasing on the Performance of Language Models in Downstream Tasks is Underestimated. IJCNLP (2) 2023: 29-36 - [i32]Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki:
Comparing Intrinsic Gender Bias Evaluation Measures without using Human Annotated Examples. CoRR abs/2301.12074 (2023) - [i31]Masahiro Kaneko, Graham Neubig, Naoaki Okazaki:
Solving NLP Problems through Human-System Collaboration: A Discussion-based Approach. CoRR abs/2305.11789 (2023) - [i30]Masahiro Kaneko, Naoaki Okazaki:
Reducing Sequence Length by Predicting Edit Operations with Large Language Models. CoRR abs/2305.11862 (2023) - [i29]Mengsay Loem, Masahiro Kaneko, Sho Takase, Naoaki Okazaki:
Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods. CoRR abs/2305.18156 (2023) - [i28]Ryuto Koike, Masahiro Kaneko, Naoaki Okazaki:
OUTFOX: LLM-generated Essay Detection through In-context Learning with Adversarially Generated Examples. CoRR abs/2307.11729 (2023) - [i27]Daisuke Oba, Masahiro Kaneko, Danushka Bollegala:
In-Contextual Bias Suppression for Large Language Models. CoRR abs/2309.07251 (2023) - [i26]Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki:
The Impact of Debiasing on the Performance of Language Models in Downstream Tasks is Underestimated. CoRR abs/2309.09092 (2023) - [i25]Panatchakorn Anantaprayoon, Masahiro Kaneko, Naoaki Okazaki:
Evaluating Gender Bias of Pre-trained Language Models in Natural Language Inference by Considering All Labels. CoRR abs/2309.09697 (2023) - [i24]Masahiro Kaneko, Naoaki Okazaki:
Controlled Generation with Prompt Insertion for Natural Language Explanations in Grammatical Error Correction. CoRR abs/2309.11439 (2023) - [i23]Mengsay Loem, Masahiro Kaneko, Naoaki Okazaki:
SAIE Framework: Support Alone Isn't Enough - Advancing LLM Training with Adversarial Remarks. CoRR abs/2311.08107 (2023) - [i22]Ryuto Koike, Masahiro Kaneko, Naoaki Okazaki:
How You Prompt Matters! Even Task-Oriented Constraints in Instructions Affect LLM-Generated Text Detection. CoRR abs/2311.08369 (2023) - 2022
- [j2]Tosho Hirasawa, Masahiro Kaneko, Aizhan Imankulova, Mamoru Komachi:
Pre-Trained Word Embedding and Language Model Improve Multimodal Machine Translation: A Case Study in Multi30K. IEEE Access 10: 67653-67668 (2022) - [c127]Masahiro Kaneko, Danushka Bollegala:
Unmasking the Mask - Evaluating Social Biases in Masked Language Models. AAAI 2022: 11954-11962 - [c126]Yi Zhou, Masahiro Kaneko, Danushka Bollegala:
Sense Embeddings are also Biased - Evaluating Social Biases in Static and Contextualised Sense Embeddings. ACL (1) 2022: 1924-1935 - [c125]Masahiro Kaneko, Sho Takase, Ayana Niwa, Naoaki Okazaki:
Interpretability for Language Learners Using Example-Based Grammatical Error Correction. ACL (1) 2022: 7176-7187 - [c124]Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki:
Debiasing Isn't Enough! - on the Effectiveness of Debiasing MLMs and Their Social Biases in Downstream Tasks. COLING 2022: 1299-1310 - [c123]Koki Maeda, Masahiro Kaneko, Naoaki Okazaki:
IMPARA: Impact-Based Metric for GEC Using Parallel Data. COLING 2022: 3578-3588 - [c122]Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki:
Gender Bias in Meta-Embeddings. EMNLP (Findings) 2022: 3118-3133 - [c121]Yujin Takahashi, Masahiro Kaneko, Masato Mita, Mamoru Komachi:
ProQE: Proficiency-wise Quality Estimation dataset for Grammatical Error Correction. LREC 2022: 5994-6000 - [c118]Mengsay Loem, Sho Takase, Masahiro Kaneko, Naoaki Okazaki:
ExtraPhrase: Efficient Data Augmentation for Abstractive Summarization. NAACL-HLT (Student Research Workshop) 2022: 16-24 - [c117]Masahiro Kaneko, Aizhan Imankulova, Danushka Bollegala, Naoaki Okazaki:
Gender Bias in Masked Language Models for Multiple Languages. NAACL-HLT 2022: 2740-2750 - [c116]Hiroyuki Deguchi, Kenji Imamura, Masahiro Kaneko, Yuto Nishida, Yusuke Sakai, Justin Vasselli, Huy Hien Vu, Taro Watanabe:
NAIST-NICT-TIT WMT22 General MT Task Submission. WMT 2022: 244-250 - [i21]Mengsay Loem, Sho Takase, Masahiro Kaneko, Naoaki Okazaki:
ExtraPhrase: Efficient Data Augmentation for Abstractive Summarization. CoRR abs/2201.05313 (2022) - [i20]Yujin Takahashi, Masahiro Kaneko, Masato Mita, Mamoru Komachi:
Proficiency Matters Quality Estimation in Grammatical Error Correction. CoRR abs/2201.06199 (2022) - [i19]Masahiro Kaneko, Sho Takase, Ayana Niwa, Naoaki Okazaki:
Interpretability for Language Learners Using Example-Based Grammatical Error Correction. CoRR abs/2203.07085 (2022) - [i18]Yi Zhou, Masahiro Kaneko, Danushka Bollegala:
Sense Embeddings are also Biased-Evaluating Social Biases in Static and Contextualised Sense Embeddings. CoRR abs/2203.07523 (2022) - [i17]Masahiro Kaneko, Aizhan Imankulova, Danushka Bollegala, Naoaki Okazaki:
Gender Bias in Masked Language Models for Multiple Languages. CoRR abs/2205.00551 (2022) - [i16]Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki:
Gender Bias in Meta-Embeddings. CoRR abs/2205.09867 (2022) - [i15]Mengsay Loem, Sho Takase, Masahiro Kaneko, Naoaki Okazaki:
Are Neighbors Enough? Multi-Head Neural n-gram can be Alternative to Self-attention. CoRR abs/2207.13354 (2022) - [i14]Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki:
Debiasing isn't enough! - On the Effectiveness of Debiasing MLMs and their Social Biases in Downstream Tasks. CoRR abs/2210.02938 (2022) - 2021
- [c115]Masahiro Kaneko, Danushka Bollegala:
Dictionary-based Debiasing of Pre-trained Word Embeddings. EACL 2021: 212-223 - [c114]Masahiro Kaneko, Danushka Bollegala:
Debiasing Pre-trained Contextualised Embeddings. EACL 2021: 1256-1266 - [c112]Raj Dabre, Aizhan Imankulova, Masahiro Kaneko:
Studying The Impact Of Document-level Context On Simultaneous Neural Machine Translation. MTSummit (1) 2021: 202-214 - [c111]Aomi Koyama, Kengo Hotate, Masahiro Kaneko, Mamoru Komachi:
Comparison of Grammatical Error Correction Using Back-Translation Models. NAACL-HLT (Student Research Workshop) 2021: 126-135 - [c110]Seiichiro Kondo, Kengo Hotate, Tosho Hirasawa, Masahiro Kaneko, Mamoru Komachi:
Sentence Concatenation Approach to Data Augmentation for Neural Machine Translation. NAACL-HLT (Student Research Workshop) 2021: 143-149 - [i13]Masahiro Kaneko, Danushka Bollegala:
Debiasing Pre-trained Contextualised Embeddings. CoRR abs/2101.09523 (2021) - [i12]Masahiro Kaneko, Danushka Bollegala:
Dictionary-based Debiasing of Pre-trained Word Embeddings. CoRR abs/2101.09525 (2021) - [i11]Raj Dabre, Aizhan Imankulova, Masahiro Kaneko, Abhisek Chakrabarty:
Simultaneous Multi-Pivot Neural Machine Translation. CoRR abs/2104.07410 (2021) - [i10]Masahiro Kaneko, Danushka Bollegala:
Unmasking the Mask - Evaluating Social Biases in Masked Language Models. CoRR abs/2104.07496 (2021) - [i9]Aomi Koyama, Kengo Hotate, Masahiro Kaneko, Mamoru Komachi:
Comparison of Grammatical Error Correction Using Back-Translation Models. CoRR abs/2104.07848 (2021) - [i8]Seiichiro Kondo, Kengo Hotate, Masahiro Kaneko, Mamoru Komachi:
Sentence Concatenation Approach to Data Augmentation for Neural Machine Translation. CoRR abs/2104.08478 (2021) - 2020
- [c109]Masahiro Kaneko, Masato Mita, Shun Kiyono, Jun Suzuki, Kentaro Inui:
Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction. ACL 2020: 4248-4254 - [c108]Masahiro Kaneko, Aizhan Imankulova, Tosho Hirasawa, Mamoru Komachi:
English-to-Japanese Diverse Translation by Combining Forward and Backward Outputs. NGT@ACL 2020: 134-138 - [c107]Zizheng Zhang, Tosho Hirasawa, Wei Houjing, Masahiro Kaneko, Mamoru Komachi:
Translation of New Named Entities from English to Chinese. WAT@AAC/IJCNLPL 2020: 58-63 - [c106]Hiroto Tamura, Tosho Hirasawa, Masahiro Kaneko, Mamoru Komachi:
TMU Japanese-English Multimodal Machine Translation System for WAT 2020. WAT@AAC/IJCNLPL 2020: 80-91 - [c105]Masahiro Kaneko, Danushka Bollegala:
Autoencoding Improves Pre-trained Word Embeddings. COLING 2020: 1699-1713 - [c104]Kengo Hotate, Masahiro Kaneko, Mamoru Komachi:
Generating Diverse Corrections with Local Beam Search for Grammatical Error Correction. COLING 2020: 2132-2137 - [c103]Ikumi Yamashita, Satoru Katsumata, Masahiro Kaneko, Aizhan Imankulova, Mamoru Komachi:
Cross-lingual Transfer Learning for Grammatical Error Correction. COLING 2020: 4704-4715 - [c102]Ryoma Yoshimura, Masahiro Kaneko, Tomoyuki Kajiwara, Mamoru Komachi:
SOME: Reference-less Sub-Metrics Optimized for Manual Evaluations of Grammatical Error Correction. COLING 2020: 6516-6522 - [c101]Masato Mita, Shun Kiyono, Masahiro Kaneko, Jun Suzuki, Kentaro Inui:
A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction. EMNLP (Findings) 2020: 267-280 - [c97]Aizhan Imankulova, Masahiro Kaneko, Tosho Hirasawa, Mamoru Komachi:
Towards Multimodal Simultaneous Neural Machine Translation. WMT@EMNLP 2020: 594-603 - [i7]Aizhan Imankulova, Masahiro Kaneko, Tosho Hirasawa, Mamoru Komachi:
Towards Multimodal Simultaneous Neural Machine Translation. CoRR abs/2004.03180 (2020) - [i6]Masahiro Kaneko, Masato Mita, Shun Kiyono, Jun Suzuki, Kentaro Inui:
Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction. CoRR abs/2005.00987 (2020) - [i5]Masato Mita, Shun Kiyono, Masahiro Kaneko, Jun Suzuki, Kentaro Inui:
A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction. CoRR abs/2010.03155 (2020) - [i4]Masahiro Kaneko, Danushka Bollegala:
Autoencoding Improves Pre-trained Word Embeddings. CoRR abs/2010.13094 (2020) - 2019
- [j1]Masahiro Kaneko, Mamoru Komachi:
Multi-Head Multi-Layer Attention to Deep Language Representations for Grammatical Error Detection. Computación y Sistemas 23(3) (2019) - [c96]Kengo Hotate, Masahiro Kaneko, Satoru Katsumata, Mamoru Komachi:
Controlling Grammatical Error Correction Using Word Edit Rate. ACL (2) 2019: 149-154 - [c95]Masahiro Kaneko, Danushka Bollegala:
Gender-preserving Debiasing for Pre-trained Word Embeddings. ACL (1) 2019: 1641-1650 - [c94]Aizhan Imankulova, Masahiro Kaneko, Mamoru Komachi:
Japanese-Russian TMU Neural Machine Translation System using Multilingual Model for WAT 2019. WAT@EMNLP-IJCNLP 2019: 165-170 - [c93]Masahiro Kaneko, Kengo Hotate, Satoru Katsumata, Mamoru Komachi:
TMU Transformer System Using BERT for Re-ranking at BEA 2019 Grammatical Error Correction on Restricted Track. BEA@ACL 2019: 207-212 - [c92]Mio Arai, Masahiro Kaneko, Mamoru Komachi:
Grammatical-Error-Aware Incorrect Example Retrieval System for Learners of Japanese as a Second Language. BEA@ACL 2019: 296-305 - [c90]Masato Mita, Tomoya Mizumoto, Masahiro Kaneko, Ryo Nagata, Kentaro Inui:
Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models - Is Single-Corpus Evaluation Enough? NAACL-HLT (1) 2019: 1309-1314 - [i3]Masato Mita, Tomoya Mizumoto, Masahiro Kaneko, Ryo Nagata, Kentaro Inui:
Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models - Is Single-Corpus Evaluation Enough? CoRR abs/1904.02927 (2019) - [i2]Masahiro Kaneko, Mamoru Komachi:
Multi-Head Multi-Layer Attention to Deep Language Representations for Grammatical Error Detection. CoRR abs/1904.07334 (2019) - [i1]Masahiro Kaneko, Danushka Bollegala:
Gender-preserving Debiasing for Pre-trained Word Embeddings. CoRR abs/1906.00742 (2019) - 2018
- [c89]Masahiro Kaneko, Tomoyuki Kajiwara, Mamoru Komachi:
TMU System for SLAM-2018. BEA@NAACL-HLT 2018: 365-369 - 2017
- [c85]Masahiro Kaneko, Yuya Sakaizawa, Mamoru Komachi:
Grammatical Error Detection Using Error- and Grammaticality-Specific Word Embeddings. IJCNLP(1) 2017: 40-48
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