Automatic sense prediction for implicit discourse relations in text

What it is about

The paper predicts the sense (Comparison, Contingency, Expansion, Temporal) of implicit discourse relations, meaning adjacent sentences with no connective, in PDTB newswire. It tests whether word-pair features from earlier work help. Word pairs from artificial implicit data (explicit connectives removed) learn the connectives that were removed, and their top pairs are function-word pairs such as "but-but", so they do not capture semantic opposition. The authors add polarity tags, Levin verb classes, first and last words, modality, number features and context (a neighbouring explicit relation or a paragraph boundary). The best model reaches F about 47 for Contingency (16 points over the word-pair baseline) and about 76 for Expansion. Evaluation uses the natural class distribution, with sections 2-20 for training and 21-22 for testing.

Ideas

• I1. Leakage audit for Mova's own training data. When the explicit connective is removed from a tale or school sentence to make a synthetic relation, check whether a pair model still sees the connective. Run the pair check first: if the top features are connective pairs, the synthetic data is leaking. Measure with two-fold cv and two identical runs.
• I2. Polarity pairs from the en lexicon as a Contingency signal. The paper finds opposite-polarity pairs help Contingency, not Comparison. Test this on our tale text, one relation at a time, and report per-relation F rather than total accuracy.
• I3. Neighbour context for implicit links in prag. If the previous link is explicit, use its connective and sense as a prior for the next implicit one. Measure on world paragraphs and report the gain only after two identical runs.
• I4. Report per-sense F and a majority baseline for every relation set, not accuracy alone. Expansion dominates the distribution, so accuracy by itself hides a weak classifier.

For Mova

The chapter is 36. Discourse relations and coherence, section 7 (implicit relations). The code is the link labels in prag and the word-pair and polarity features would live in a new module next to them. The paper's datasets (PDTB, TextRels, the Wilson polarity lexicon and the General Inquirer) need a licence check before use; the paper's own experiments are research-only, so use them as a reference, not as training data in a release.

Topics: Discourse analysis

Cite

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

@inproceedings{pitler2009automatic,
  title = {{Automatic sense prediction for implicit discourse relations in text}},
  author = {Pitler, Emily and Louis, Annie and Nenkova, Ani},
  booktitle = {Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP},
  year = {2009},
  url = {https://aclanthology.org/P09-1077/}
}

References

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