![]() ![]() Our approach improves dialog act prediction and semantic role labeling by 1.3% and 2.5% in F1 score respectively in social conversations. Finally, we combine the prediction results from these two utterances using a selection model that is guided by expert knowledge. We then train a prediction model using both utterances containing ellipsis and our automatically completed utterances. Specifically, we first complete user utterances to resolve ellipsis using an end-to-end pointer network model. To address this issue, we propose a method which considers both the original utterance that has ellipsis and the automatically completed utterance in dialog act and semantic role labeling tasks. However, automatic ellipsis completion can result in output which does not accurately reflect user intent. We propose to resolve ellipsis through automatic sentence completion to improve language understanding. Ellipsis increases the difficulty of a series of downstream language understanding tasks, such as dialog act prediction and semantic role labeling. The phenomenon of ellipsis is prevalent in social conversations. ![]()
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