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

本文提出了 DiscProReco，一种神经模型，用于联合恢复 DPR 和对话语篇分析 CDP，并进一步证明 DPR 和 CDP 之间的相关性。DiscProReco 利用有向图卷积网络来编码对话中的 utterance，使用 biaffine 分类器构建对话语篇图，并使用 multi-relational GCN 来产生增强过的语篇关系，同时结合了 token 状态和 utterance 状态作为 dropped pronoun recovery 的输入。实验结果表明，DiscProReco 明显优于两种任务的最先进基线。