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Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification

Summary

Identifying implicit discourse relations, where no connective such as "because" or "but" signals the link between two text spans, is a major bottleneck in discourse parsing. Shi and Demberg tackle this with a model that copies how PDTB annotators worked: annotators first inserted a fitting connective and only then chose a relation label. The model pairs a classifier with a seq2seq network. The network reads [Arg1; Arg2] and learns to output [Arg1; implicit connective; Arg2], using the connectives annotated in PDTB 2.0. Its parts are a bidirectional LSTM encoder over Word2Vec embeddings, a separate LSTM decoder with Luong-style global attention, a gated-interaction layer, and a context memory network that retrieves similar training instances. The classifier combines the original argument encoding, the "explicitated" decoder representation, and the memory-derived knowledge vector. The authors report state-of-the-art results on the standard 11-way PDTB classification, including under cross-validation, and strong results on top-level 4-way and binary tasks. This suggests that generating a connective as an auxiliary task produces more discriminative representations of the arguments.

Key points

  • Auxiliary task: reconstruct the arguments with the annotated implicit connective inserted, so the input and output sequences differ. This distinguishes the model from a plain autoencoder and from the adversarial connective approach of Qin et al. (2017).
  • Data: PDTB 2.0 implicit relations, with 12,345 training, 1,156 development and 1,011 test instances at the top level. Only 55 Temporal instances appear in the test set.
  • Top-level 4-way results: 61.42% accuracy (the best reported) and 46.40 macro-F1 (second best).
  • One-vs-all binary F1: Comparison 41.83, Contingency 62.07, Expansion 69.58, Temporal 35.72. The model beats prior systems on Comparison and Contingency.
  • Analysis and outlook: memory attention tends to select training examples with the same relation. The gains are not explained merely by a larger parameter count. The authors see potential for connective prediction and argument generation.

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

10 papers of our library cite it (of the 140673 whose bibliographies we read); 5 of them have a page here, 5 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/w19-0416 · arXiv 1811.01697

@inproceedings{shi2019learning,
  title = {{Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification}},
  author = {Shi, Wei and Demberg, Vera},
  booktitle = {Proceedings of the 13th International Conference on Computational Semantics - Long Papers},
  year = {2019},
  doi = {10.18653/v1/w19-0416},
  eprint = {1811.01697},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/w19-0416}
}

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