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Discourse parsing for multi-party chat dialogues

Summary

The paper presents what the authors describe as the first discourse parser for multi-party chat. This genre is hard because several conversation threads interleave, links between units often cross or span long distances, and some units have more than one parent. Chat also has fragmentary syntax and few explicit connectives, so syntactic cues help little. The authors use SDRT, which allows non-adjacent attachments, complex discourse units (CDUs) and graphs that are not trees. They annotated chats from online games of The Settlers of Catan to build the STAC corpus. To fit a dependency-parsing framework, they replace each CDU with its recursive head. They then train a local model that estimates attachment and relation probabilities for pairs of units and decode it into a global structure. The best setup applies a turn constraint: within a turn, units attach to the preceding unit (Last), and an MST decoder links turns via their heads. The resulting scores rival or exceed monologue parsers, even though the input is noisier and less syntactic.

Key points

  • STAC corpus: 59 games collected, 36 annotated in SDRT style. This yields 1091 dialogues, 10677 EDUs and 11348 relations, with roughly 10% (123 dialogues) held out for testing.
  • At least 9% of links belong to non-tree structures, and crossing dependencies are frequent. This makes RST-style adjacency and right-frontier assumptions unsuitable for this genre.
  • Global model results: undirected attachment F1 = 0.680, directed attachment F1 = 0.671, fully labelled structure F1 = 0.516. Intra-turn attachment alone reaches F1 0.892 (undirected).
  • Comparison with monologue parsers:
    • The labelled score beats Muller et al. (2012), at 0.361, and Li et al. (2014), at 0.4309.
    • It is close to Joty et al. (2015), at 0.689 unlabelled and 0.5587 labelled, using considerably simpler methods.

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

40 papers of our library cite it (of the 140673 whose bibliographies we read); 12 of them have a page here, 28 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/d15-1109

@inproceedings{afantenos2015discourse,
  title = {{Discourse parsing for multi-party chat dialogues}},
  author = {Afantenos, Stergos and Kow, Eric and Asher, Nicholas and Perret, Jérémy},
  booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
  year = {2015},
  doi = {10.18653/v1/d15-1109},
  url = {https://doi.org/10.18653/v1/d15-1109}
}

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