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Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph

Summary

Implicit discourse relations, which have no connective such as "because", are hard to classify. Most earlier systems examine only the two discourse units (DUs) involved and ignore the rest of the paragraph. Dai and Huang propose a paragraph-level neural model for the PDTB corpus. It encodes a whole paragraph at once and labels every relation between adjacent DUs, both implicit and explicit.

The architecture has two levels:

  • A word-level Bi-LSTM, followed by max-pooling, produces one vector per DU.
  • A second, DU-level Bi-LSTM lets each unit's representation absorb information from its neighbours.

Two additions improve results further. Separate (untied) prediction layers handle explicit and implicit relations, and a CRF layer captures typical sequences of relation labels. The full model reaches 48.82% macro-F1 on four-way implicit classification, beating the strongest prior system. Explicit relations also improve slightly.

The work shows that wider context and the paragraph's overall discourse structure help recognize difficult implicit relations. Gains appear even for short paragraphs.

Key points

  • Input embeddings are 343-dimensional: 300-d Google word2vec, 36-d one-hot POS tags and 7-d named entity tags, all produced with Stanford CoreNLP.
  • On implicit relations, the full model's 48.82% macro-F1 beats Lan et al. (2017) by about 1 point and neural tensor models such as Lei et al. (2017) by over 2 points.
  • In binary classification, the model with untied parameters gains about 6 F1 points on Comparison and 7 on Temporal over the best previous systems.
  • Majority-vote ensembles of 10 runs raise macro-F1 to 51.84% for implicit and 94.17% for explicit relations.
  • Paragraph-level variants beat DU-pair baselines across all paragraph lengths. Gains are largest for paragraphs with more than 5 DUs.

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

31 papers of our library cite it (of the 140673 whose bibliographies we read); 17 of them have a page here, 14 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/n18-1013 · arXiv 1804.05918

@inproceedings{dai2018improving,
  title = {{Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph}},
  author = {Dai, Zeyu and Huang, Ruihong},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
  year = {2018},
  doi = {10.18653/v1/n18-1013},
  eprint = {1804.05918},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/n18-1013}
}

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