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

Summary

Chinese speakers frequently omit pronouns that listeners can infer from context. Existing systems handle two related problems separately: dropped pronoun recovery (DPR), which finds where a pronoun is missing and what type it is, and conversational discourse parsing (CDP), which links utterances through relations such as reply or question. The authors argue that each task can help the other. Discourse structure indicates which earlier turns hold the referent, while missing pronouns make utterances incomplete and harder to parse.

They propose DiscProReco, a jointly trained neural network with four stages:

  • A directed GCN encodes each utterance's dependency tree.
  • A biaffine classifier predicts relations between pairs of utterances and builds a discourse graph.
  • A multi-relational GCN feeds that graph structure back into the token representations.
  • A sequence-labeling layer recovers the dropped pronouns.

Because no existing corpus covered both phenomena, the authors annotated a new dataset, SPDPR. DiscProReco beats strong baselines for both tasks on SPDPR and on other benchmarks. This shows that pronoun recovery and discourse analysis are worth modeling together in dialogue understanding.

Key points

  • DPR is treated as sequence labeling. Each token gets one of 17 labels indicating which pronoun type, if any, was dropped before it.
  • The model uses Stanza (Qi et al., 2020) to parse each utterance into dependencies, a gated SynGCN over those parses, and a RelGCN over the predicted discourse graph. Biaffine scoring follows Dozat and Manning (2017).
  • SPDPR (Structure Parsing-enhanced Dropped Pronoun Recovery) is presented as the first corpus annotated with both dropped pronouns and discourse relations. The corpus and code are released at github.com/ningningyang/DiscProReco.
  • On STAC (CDP only), DiscProReco is compared with discourse parsers such as Deep Sequential (Shi and Huang, 2019). On SPDPR, the full joint model outperforms its single-task variant DiscProReco (w/o DPR).
  • In analysis experiments, DPR quality rose steadily as the CDP arc F-score was raised from 30.64 to 50.38. When the authors varied the loss-weight ratio α/β from 0.25 to 1.25, CDP stayed stable while DPR dropped sharply at high ratios, which suggests the training objective should favor DPR.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Topics: Discourse analysis · Dependency parsing

Cited by

7 papers of our library cite it (of the 140673 whose bibliographies we read); 3 of them have a page here, 4 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2021.acl-long.138 · arXiv 2106.03345

@inproceedings{yang2021joint,
  title = {{A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech}},
  author = {Yang, Jingxuan and Xu, Kerui and Xu, Jun and Li, Si and Gao, Sheng and Guo, Jun and Xue, Nianwen and Wen, Ji-Rong},
  booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  year = {2021},
  doi = {10.18653/v1/2021.acl-long.138},
  eprint = {2106.03345},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2021.acl-long.138}
}

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