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Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings

Summary

The paper addresses implicit discourse relation recognition (IDRR): deciding how two adjacent text spans relate (e.g., Cause, Contrast) when no explicit connective links them. In the Penn Discourse Treebank (PDTB), annotators insert an "implicit connective" for each argument pair, and each connective maps to a small set of relations. Earlier multi-task models predicted both labels through shared parameters but did not use this mapping.

The authors build on the encoder of Bai and Zhao (2018), which combines word2vec, ELMo and subword inputs with gated, residual CNNs and co-attention. They replace the plain softmax heads with learned embedding matrices for relations and connectives in a shared space. Three penalty terms shape these embeddings:

  • one pulls each connective toward the relations it can express;
  • one keeps connectives of the same relation orthogonal, which prevents collapse;
  • one makes relation embeddings mutually diverse.

The model beats its base system on every PDTB setting and sets new state-of-the-art results for multi-class classification. This shows that label embeddings can carry knowledge from auxiliary labels to the main task.

Key points

  • Data: PDTB, where 53% of implicit connectives correspond to exactly one relation and 44% to two. Connective labels are needed only during training.
  • Results on multi-class settings (vs. Bai and Zhao, 2018):
    • 4-way F1: 53.00 vs 51.06
    • PDTB-Lin 11-way accuracy: 46.48 vs 45.73
    • PDTB-Ji 11-way accuracy: 49.95 vs 48.22
    • All gains are significant at p < 0.05.
  • Binary one-vs-other F1: best reported scores for Comparison (48.44) and Expansion (73.66). Contingency (56.84) and Temporal (38.60) are not the top results.
  • Ablation (4-way F1): adding embeddings alone gives 51.62, a small gain. Every penalty term contributes, and using all three is best.
  • Setup: embedding size d = 80, Adagrad with learning rate 0.001, and loss weights tuned separately for each task setting.

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

18 papers of our library cite it (of the 140673 whose bibliographies we read); 12 of them have a page here, 6 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/p19-1411

@inproceedings{nguyen2019employing,
  title = {{Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings}},
  author = {Nguyen, Linh The and Ngo, Linh Van and Than, Khoat and Nguyen, Thien Huu},
  booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
  year = {2019},
  doi = {10.18653/v1/p19-1411},
  url = {https://doi.org/10.18653/v1/p19-1411}
}

References

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