Papers by Atsushi Otsuka
Multi-style Generative Reading Comprehension (P19-1)
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Kyosuke Nishida, Itsumi Saito, Kosuke Nishida, Kazutoshi Shinoda, Atsushi Otsuka, Hisako Asano, Junji Tomita
| Challenge: | Current studies on generative reading comprehension (RC) focus on extracting an answer span from textual evidence and natural language generation (NLG). |
| Approach: | They propose a multi-style abstractive summarization model for question answering called Masque. |
| Outcome: | The proposed model achieves state-of-the-art performance on the Q&A and Q& A + NLG tasks of MS MARCO and NarrativeQA. |
Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction (P19-1)
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Kosuke Nishida, Kyosuke Nishida, Masaaki Nagata, Atsushi Otsuka, Itsumi Saito, Hisako Asano, Junji Tomita
| Challenge: | Question answering (QA) using textual sources for purposes such as reading comprehension has attracted much attention. |
| Approach: | They propose a Query Focused Extractor model for evidence extraction and multi-task learning with the QA model. |
| Outcome: | The proposed model achieves state-of-the-art evidence extraction score on hotpotQA and FEVER, which is a recognizing textual entailment task on a large textual database. |
Analysis of Sensation-transfer Dialogues in Motorsports (2024.lrec-main)
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| Challenge: | a recent study has examined the effects of subjective ideas on group performance in motorsports. |
| Approach: | They collected dialogues between drivers and engineers in motorsports to test this hypothesis . they defined "sensation" as a unique event unfolding in the mind of a speaker . |
| Outcome: | The results show that the more subjective information interlocutors exchange, the better the group performance in collaborative work. |