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Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading Comprehension

8 Sep 2021Findings (EMNLP) 2021 11arXiv:2109.03772archive 2025-07-28

Yiyang Li, Hai Zhao

Multi-party dialogue machine reading comprehension (MRC) brings tremendous challenge since it involves multiple speakers at one dialogue, resulting in intricate speaker information flows and noisy dialogue contexts. To alleviate such difficulties, previous models focus on how to incorporate these information using complex graph-based modules and additional manually labeled data, which is usually rare in real scenarios. In this paper, we design two labour-free self- and pseudo-self-supervised prediction tasks on speaker and key-utterance to implicitly model the speaker information flows, and capture salient clues in a long dialogue. Experimental results on two benchmark datasets have justified the effectiveness of our method over competitive baselines and current state-of-the-art models.

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ericlee8/multi-party-dialogue-mrc officialmentioned in paperpytorch report

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Tasks

Machine Reading ComprehensionQuestion AnsweringReading Comprehension

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Question Answering FriendsQA Li and Zhao - ELECTRA EM 55.8 #2 of 6 Archive leaderboard report
Question Answering FriendsQA Li and Zhao - ELECTRA F1 72.3 #2 of 6 Archive leaderboard report
Question Answering FriendsQA Li and Zhao - BERT EM 46.9 #4 of 6 Archive leaderboard report
Question Answering FriendsQA Li and Zhao - BERT F1 63.9 #4 of 6 Archive leaderboard report
Question Answering Molweni Li and Zhao - ELECTRA EM 58 #2 of 4 Archive leaderboard report
Question Answering Molweni Li and Zhao - ELECTRA F1 72.9 #2 of 4 Archive leaderboard report
Question Answering Molweni Li and Zhao - BERT EM 49.2 #3 of 4 Archive leaderboard report
Question Answering Molweni Li and Zhao - BERT F1 64 #3 of 4 Archive leaderboard report

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