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Structured Dialogue Discourse Parsing

Summary

The paper addresses dialogue discourse parsing in the SDRT style. Given the utterances (EDUs) of a multi-party conversation, the parser must find which earlier utterance each one attaches to and label that link with one of 17 discourse relations. Earlier parsers scored utterance pairs mostly in isolation, predicted links and relations in separate steps, and often depended on speaker and turn features. The authors add a dummy root utterance and drop a small share of edges. This turns each annotated dialogue into a labeled, multi-root, non-projective spanning tree. Pairwise utterance representations come from BERT and are contextualized with bidirectional LSTMs over the adjacency matrix. Training maximizes the probability of the gold tree over all candidate trees using the directed matrix-tree theorem. Decoding applies a modified Chu-Liu-Edmonds (CLE) algorithm that predicts links and relations jointly. The model uses no hand-crafted features. It sets a new state of the art on STAC and Molweni and holds up better on long dialogues and when speaker information is missing. This makes it attractive for building parsers on new dialogue data.

Key points

  • Evaluated on the STAC and Molweni corpora, where it beats the previous best by 2.3 F1 on STAC and 1.5 F1 on Molweni.
  • Reducing the graphs to trees discards about 6% of edges in STAC and none in Molweni. When an utterance has several parents, only the most recent one is kept, and utterances with no parent are attached to the dummy root.
  • Links and relations share one three-dimensional score tensor, so the parser predicts them together rather than in two stages. The authors also propose a padding trick that allows determinant computation over variable-length dialogues in a batch.
  • Without speaker and turn information, LAS on STAC falls from 59.6 to 54.4. The strongest baseline, Struct-Aware, drops more sharply, from 57.3 to 47.8. The model's advantage also grows as dialogues get longer.
  • Code is released at github.com/chijames/structured_dialogue_discourse_parsing.

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

Cited by

16 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 12 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2022.sigdial-1.32 · arXiv 2306.15103

@inproceedings{chi2022structured,
  title = {{Structured Dialogue Discourse Parsing}},
  author = {Chi, Ta-Chung and Rudnicky, Alexander},
  booktitle = {Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue},
  year = {2022},
  doi = {10.18653/v1/2022.sigdial-1.32},
  eprint = {2306.15103},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2022.sigdial-1.32}
}

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