該任務(wù)現(xiàn)有的數(shù)據(jù)集主要有如下這些： 1. Ma et, al, 2018, NAACL（數(shù)據(jù)集沒有命名） 任務(wù)類型：完形填空 論文：Challenging reading comprehension on daily conversation: Passage completion on multiparty dialog. 數(shù)據(jù)集：GitHub - emorynlp/reading-comprehension: Reading comprehension on multiparty dialog. 2.?DREAM, TACL 2019 任務(wù)類型：單選題 論文：Dream: A challenge data set and models for dialogue-based reading comprehension. 數(shù)據(jù)集：A Challenge Dataset and Models for Dialogue-Based Reading Comprehension 3.?FriendsQA, SIGDial 2019 任務(wù)類型：Span-base 論文：FriendsQA: Open-Domain Question Answering on TV Show Transcripts 數(shù)據(jù)集：GitHub - emorynlp/FriendsQA: Question answering on multiparty dialogue 4.?Molweni，COLING 2020 任務(wù)類型：Span-based 論文：Molweni: A Challenge Multiparty Dialogue-based Machine Reading Comprehension Dataset with Discourse Structure 數(shù)據(jù)集：GitHub - HIT-SCIR/Molweni 5.?QAConv, arXiv 2021 任務(wù)類型：Span-based 論文：QAConv: Question Answering on Informative Conversations 數(shù)據(jù)集：GitHub - salesforce/QAConv: This repository maintains the QAConv dataset, a question-answering dataset on informative conversations including business emails, panel discussions, and work channels. 目前此任務(wù)上使用比較多的數(shù)據(jù)集主要是DREAM、FriendsQA和Molweni。在QAConv數(shù)據(jù)集論文中，作者還將現(xiàn)有的幾個數(shù)據(jù)集進行了對比。數(shù)據(jù)集對比，來自QAConv論文
二、模型
這部分主要推薦DREAM、FriendsQA和Molweni這3個數(shù)據(jù)集上比較有代表性的模型論文。 1. DREAM數(shù)據(jù)集相關(guān)模型論文推薦a.?DUMA: Reading Comprehension with Transposition Thinking. arXiv 2020.b.?Multi-task Learning with Multi-head Attention for Multi-choice Reading Comprehension. arXiv 2020. 2.?FriendsQA數(shù)據(jù)集相關(guān)模型論文推薦a.?Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering. ACL 2020.b.?Graph-based knowledge integration for question answering over dialogue. COLING 2020. 3.?Molweni數(shù)據(jù)集相關(guān)模型論文推薦a.?DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehension. IJCNN 2021.b.?Self-and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading Comprehension. EMNLP 2021 Findings.c.?Enhanced Speaker-aware Multi-party Multi-turn Dialogue Comprehension. arXiv 2021. 以上是我簡單整理的Dialogue MRC任務(wù)數(shù)據(jù)集和推薦的幾篇相關(guān)論文，歡迎補充！
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