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Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings

Summary

Implicit discourse relations are links between two text spans (Arg1, Arg2) that lack an overt connective such as "so" or "but". They are the hardest part of discourse parsing. Annotated data is scarce, so models built on word-level features or embeddings struggle, particularly with rare words. Qin, Zhang and Zhao propose a hybrid neural classifier that adds character-derived vectors to ordinary word embeddings. In the character module, a CNN with max-pooling reads each word's characters. The resulting word sequence then goes through bidirectional LSTMs, so every vector reflects its sentence context. These vectors are joined with standard word embeddings to form "enhanced embeddings". A second CNN turns them into argument representations, followed by hidden layers and a softmax classifier. Experiments on the Penn Discourse Treebank (PDTB) cover multi-class and binary sense classification, with ablations comparing word-only, character-only and combined variants. Fusing the two sources at the embedding level outperformed every variant and matched or exceeded prior state of the art. The study shows that context-sensitive subword information benefits a semantic task where data sparsity is a central obstacle.

Key points

  • Data and evaluation: Experiments use the PDTB. The binary setup trains on Sections 2–20, develops on 0–1 and tests on 21–22. Training negatives are randomly sampled to balance classes, and scores are reported as Macro-F1.
  • Binary F1 of Char+Word-Enhanced:
    • Comparison: 38.67
    • Contingency: 54.91
    • Expansion+EntRel: 80.66
    • Temporal: 32.76
    • Average: 51.75, slightly above Chen et al. (2016) at 51.72 and Rutherford & Xue (2014) at 50.81. Only on Comparison does it trail Chen et al. (40.17).
  • Ablation findings:
    • Character-only embeddings did worse than simply summing word vectors.
    • Stacking BiLSTMs on the character CNN improved results.
    • Word-level BiLSTMs using only the last state lagged behind CNN.
    • Concatenating at the sentence level was weaker than merging at the embedding level.
  • Character module details: The alphabet covers upper- and lowercase letters, digits and punctuation. The module applies several filter widths with tanh activation, followed by one-max pooling.

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

13 papers of our library cite it (of the 140673 whose bibliographies we read); 10 of them have a page here, 3 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{qin2016implicit,
  title = {{Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings}},
  author = {Qin, Lianhui and Zhang, Zhisong and Zhao, Hai},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  year = {2016},
  url = {https://aclanthology.org/C16-1180/}
}

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