论文标题
Molweni：挑战基于多方对话的机器阅读理解数据集具有话语结构
Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure
论文作者
论文摘要
近年来，对多方对话领域的研究已经大大增长。我们介绍了Molweni数据集，这是一种机器阅读理解（MRC）数据集，该数据集具有通过多方对话框构建的话语结构。 Molweni的来源样本来自Ubuntu Chat语料库，其中包括10,000个对话，其中包括88,303台话语。我们注释有关该语料库的30,066个问题，包括回答和无法回答的问题。 Molweni还为修改后的分段话语表示理论（SDRT; Asher等，2016）样式中的所有多阶层对话框中的样式中唯一贡献了话语的依赖性注释，从而促进了大规模（78,245个注释的话语关系）数据，以承担多派对对话的任务。我们的实验表明，Molweni是当前MRC模型的具有挑战性的数据集：当前，强大的Squad 2.0表演者Bert-WWM，在Molweni的问题上仅获得67.7％的F1，与其Squad 2.0表现相比，20+％的显着下降。
Research into the area of multiparty dialog has grown considerably over recent years. We present the Molweni dataset, a machine reading comprehension (MRC) dataset with discourse structure built over multiparty dialog. Molweni's source samples from the Ubuntu Chat Corpus, including 10,000 dialogs comprising 88,303 utterances. We annotate 30,066 questions on this corpus, including both answerable and unanswerable questions. Molweni also uniquely contributes discourse dependency annotations in a modified Segmented Discourse Representation Theory (SDRT; Asher et al., 2016) style for all of its multiparty dialogs, contributing large-scale (78,245 annotated discourse relations) data to bear on the task of multiparty dialog discourse parsing. Our experiments show that Molweni is a challenging dataset for current MRC models: BERT-wwm, a current, strong SQuAD 2.0 performer, achieves only 67.7% F1 on Molweni's questions, a 20+% significant drop as compared against its SQuAD 2.0 performance.
