Papers by Itsumi Saito
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. |
How Well Do Vision Models Encode Diagram Attributes? (2024.acl-srw)
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Haruto Yoshida, Keito Kudo, Yoichi Aoki, Ryota Tanaka, Itsumi Saito, Keisuke Sakaguchi, Kentaro Inui
| Challenge: | Experimental results show vision models struggle to identify diagram attributes such as node colors and shapes, along with edge colors and connection patterns. |
| Approach: | They evaluated vision models and retrieving diagrams using text queries to determine how well they recognize diagram attributes and edge connection patterns. |
| Outcome: | The models can recognize node colors, shapes, and edge colors, but struggle to identify differences in edge connection patterns that play a pivotal role in the semantics of diagrams. |
Unsupervised Domain Adaptation of Language Models for Reading Comprehension (2020.lrec-1)
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| Challenge: | State-of-the-art reading comprehension models do not have general linguistic intelligence . accuracy of out-domain datasets is affected by the distribution of data . |
| Approach: | They propose to use supervised RC training data in the source domain and unlabeled passages in the target domain to adapt models. |
| Outcome: | The proposed model outperforms the model without domain adaptation with five datasets in different domains. |