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In this paper, we present a neural model for joint dropped pronoun recovery(DPR) and conversational discourse parsing (CDP) in Chinese conversationalspeech. We show that DPR and CDP are closely related, and a joint modelbenefits both tasks. We refer to our model as DiscProReco, and it first encodesthe tokens in each utterance in a conversation with a directed GraphConvolutional Network (GCN). The token states for an utterance are thenaggregated to produce a single state for each utterance. The utterance statesare then fed into a biaffine classifier to construct a conversational discoursegraph. A second (multi-relational) GCN is then applied to the utterance statesto produce a discourse relation-augmented representation for the utterances,which are then fused together with token states in each utterance as input to adropped pronoun recovery layer. The joint model is trained and evaluated on anew Structure Parsing-enhanced Dropped Pronoun Recovery (SPDPR) dataset that weannotated with both two types of information. Experimental results on the SPDPRdataset and other benchmarks show that DiscProReco significantly outperformsthe state-of-the-art baselines of both tasks.

A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech