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Reducing Sparsity Improves the Recognition of Implicit Discourse Relations

Summary

Li and Nenkova investigate the effect of feature sparsity on recognizing implicit relations between sentences, using the Penn Discourse Treebank (PDTB) as their data. Implicit relations link adjacent sentences without an explicit connective. They focus on the two strongest feature families from earlier work: lexical word pairs and syntactic production rules. Using only lexical and syntactic information from gold Penn Treebank parses, they train binary SVM classifiers for seven relation classes.

They show that how features are encoded matters. Recording which words occur in each argument, instead of taking the cross-product of words across arguments, yields far fewer and denser features and better results. This explains part of the advantage earlier credited to syntax alone. They also propose a new, denser syntactic representation called production sticks, which significantly improves recognition. Removing lexical content from syntactic features lowers performance for most relations, so word-level information remains essential. Agreement analysis shows that different lexical encodings make quite different errors. This suggests that combining them in future discourse parsers could help.

Key points

  • Setup: Training uses PDTB sections 2–19 and testing uses the enlarged sections 20–24. The classifiers are SVMLight with a linear kernel, with a balanced (downsampled) negative class.
    • Seven relations are covered: Comparison, Contingency, Expansion, Temporal, and the Expansion subtypes Conjunction, Instantiation and Restatement.
  • Lexical features: Word pairs produce 92,128 features, compared with 12,116 for binary-lexical. Moving to binary-lexical raises average F from 29.46 to 31.79 and accuracy from 57.22 to 60.42. Instantiation gains 7.7 F points.
  • Sparsity: The average rate of feature occurrence is:
    • sticks: 0.00623
    • prodrules: 0.00374
    • binary-lexical: 0.00276
    • word-pairs: 0.00113
  • Lexicalization: A purely syntactic variant of the sticks representation (sticks-nolex) performs worse on most relations. Lexical items also make up many of the most predictive features.
  • Classifier agreement: Q-statistic analysis shows that word-pairs and binary-lexical disagree on at least about 25% of test instances. Sticks and prodrules, by contrast, agree closely.

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); 8 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 10.3115/v1/w14-4327

@inproceedings{li2014reducing,
  title = {{Reducing Sparsity Improves the Recognition of Implicit Discourse Relations}},
  author = {Li, Junyi Jessy and Nenkova, Ani},
  booktitle = {Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)},
  year = {2014},
  doi = {10.3115/v1/w14-4327},
  url = {https://doi.org/10.3115/v1/w14-4327}
}

References

14 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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  10. Joonsuk Park and Claire Cardie. 2012. Improving implicit discourse relation recognition through feature set optimization. In Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), pages 108–112. find our card
  11. Emily Pitler, Annie Louis, and Ani Nenkova. 2009. Automatic sense prediction for implicit discourse relations in text. In 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 (ACL-IJCNLP), pag… find our card
  12. Rashmi Prasad, Nikhil Dinesh, Alan Lee, Eleni Miltsakaki, Livio Robaldo, Aravind Joshi, and Bonnie Webber. 2008. The Penn Discourse TreeBank 2.0. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC). find our card
  13. Caroline Sporleder and Alex Lascarides. 2008. Using automatically labelled examples to classify rhetorical relations: An assessment. Natural Language Engineering, 14(3):369–416, July. find
  14. Zhi-Min Zhou, Yu Xu, Zheng-Yu Niu, Man Lan, Jian Su, and Chew Lim Tan. 2010. Predicting discourse connectives for implicit discourse relation recognition. In Proceedings of the 23rd International Conference on Computational Linguistics (COLING), pages 1507–1514. find our card

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