![]() ![]() OurĪpproach improves dialog act prediction and semantic role labeling by 1.3% andĢ.5% in F1 score respectively in social conversations. Utterances using a selection model that is guided by expert knowledge. Finally, we combine the prediction results from these two Model using both utterances containing ellipsis and our automatically completed Specifically, we first complete user utterances to resolveĮllipsis using an end-to-end pointer network model. Propose a method which considers both the original utterance that has ellipsisĪnd the automatically completed utterance in dialog act and semantic role Which does not accurately reflect user intent. However, automatic ellipsis completion can result in output Resolve ellipsis through automatic sentence completion to improve language Tasks, such as dialog act prediction and semantic role labeling. Increases the difficulty of a series of downstream language understanding Download a PDF of the paper titled Filling Conversation Ellipsis for Better Social Dialog Understanding, by Xiyuan Zhang and 4 other authors Download PDF Abstract: The phenomenon of ellipsis is prevalent in social conversations. ![]()
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