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pro vyhledávání: '"Koreeda, Yuta"'
Using token representation from bidirectional language models (LMs) such as BERT is still a widely used approach for token-classification tasks. Even though there exist much larger unidirectional LMs such as Llama-2, they are rarely used to replace t
Externí odkaz:
http://arxiv.org/abs/2408.09640
Publikováno v:
In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, October 21-25, 2023, Birmingham, United Kingdom. ACM, New York, NY, USA, 5 pages
Writing a readme is a crucial aspect of software development as it plays a vital role in managing and reusing program code. Though it is a pain point for many developers, automatically creating one remains a challenge even with the recent advancement
Externí odkaz:
http://arxiv.org/abs/2308.03099
Autor:
Koreeda, Yuta, Yokote, Ken-ichi, Ozaki, Hiroaki, Yamaguchi, Atsuki, Tsunokake, Masaya, Sogawa, Yasuhiro
This paper explains the participation of team Hitachi to SemEval-2023 Task 3 "Detecting the genre, the framing, and the persuasion techniques in online news in a multi-lingual setup.'' Based on the multilingual, multi-task nature of the task and the
Externí odkaz:
http://arxiv.org/abs/2303.01794
Autor:
Liang, Percy, Bommasani, Rishi, Lee, Tony, Tsipras, Dimitris, Soylu, Dilara, Yasunaga, Michihiro, Zhang, Yian, Narayanan, Deepak, Wu, Yuhuai, Kumar, Ananya, Newman, Benjamin, Yuan, Binhang, Yan, Bobby, Zhang, Ce, Cosgrove, Christian, Manning, Christopher D., Ré, Christopher, Acosta-Navas, Diana, Hudson, Drew A., Zelikman, Eric, Durmus, Esin, Ladhak, Faisal, Rong, Frieda, Ren, Hongyu, Yao, Huaxiu, Wang, Jue, Santhanam, Keshav, Orr, Laurel, Zheng, Lucia, Yuksekgonul, Mert, Suzgun, Mirac, Kim, Nathan, Guha, Neel, Chatterji, Niladri, Khattab, Omar, Henderson, Peter, Huang, Qian, Chi, Ryan, Xie, Sang Michael, Santurkar, Shibani, Ganguli, Surya, Hashimoto, Tatsunori, Icard, Thomas, Zhang, Tianyi, Chaudhary, Vishrav, Wang, William, Li, Xuechen, Mai, Yifan, Zhang, Yuhui, Koreeda, Yuta
Publikováno v:
Published in Transactions on Machine Learning Research (TMLR), 2023
Language models (LMs) are becoming the foundation for almost all major language technologies, but their capabilities, limitations, and risks are not well understood. We present Holistic Evaluation of Language Models (HELM) to improve the transparency
Externí odkaz:
http://arxiv.org/abs/2211.09110
Autor:
Koreeda, Yuta, Manning, Christopher D.
Reviewing contracts is a time-consuming procedure that incurs large expenses to companies and social inequality to those who cannot afford it. In this work, we propose "document-level natural language inference (NLI) for contracts", a novel, real-wor
Externí odkaz:
http://arxiv.org/abs/2110.01799
Autor:
Koreeda, Yuta, Manning, Christopher D.
While many NLP pipelines assume raw, clean texts, many texts we encounter in the wild, including a vast majority of legal documents, are not so clean, with many of them being visually structured documents (VSDs) such as PDFs. Conventional preprocessi
Externí odkaz:
http://arxiv.org/abs/2105.00150
Autor:
Ravikiran, Manikandan, Muljibhai, Amin Ekant, Miyoshi, Toshinori, Ozaki, Hiroaki, Koreeda, Yuta, Masayuki, Sakata
In this paper, we present our participation in SemEval-2020 Task-12 Subtask-A (English Language) which focuses on offensive language identification from noisy labels. To this end, we developed a hybrid system with the BERT classifier trained with twe
Externí odkaz:
http://arxiv.org/abs/2005.00295
Publikováno v:
in Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning
This paper describes the proposed system of the Hitachi team for the Cross-Framework Meaning Representation Parsing (MRP 2019) shared task. In this shared task, the participating systems were asked to predict nodes, edges and their attributes for fiv
Externí odkaz:
http://arxiv.org/abs/1910.01299
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