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QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines

Summary

Annotating discourse relations, meaning how two propositions within a text connect (causally, temporally, contrastively and so on), has traditionally required trained experts working with schemes such as PDTB or RST. That makes such annotation slow and expensive. This paper extends the QA-based semantic annotation approach (as in QASRL and QAMR) to discourse. The authors encode an intra-sentential relation as a question–answer pair. The question prefix (e.g., "Despite what…?", "In what case…?") signals the relation sense, and the question body and the answer hold the two discourse units. The prefixes are mapped onto most of the PDTB 3.0 sense taxonomy and can capture both explicit and implicit relations. Through a dedicated crowdsourcing interface, non-expert workers produced the QADiscourse dataset. The authors also built a pipeline parser as a baseline. It achieves fairly high unlabeled QA alignment, but it picks the correct relation prefix only about half the time. The work matters because it suggests discourse annotation can be scaled up with laymen and adapted to other languages and domains.

Key points

  • The QADiscourse dataset covers roughly 9,000 sentences and over 16,600 QA pairs, and the authors released it openly.
  • The question templates follow a subset of PDTB 3.0 senses:
    • Pragmatic variants (Belief, SpeechAct) are merged into their general senses.
    • Purpose is folded into the two causal questions.
    • Three Expansion senses are left out, including the very frequent Expansion.Conjunction, because they assert no informational proposition.
  • The baseline is a pipeline with three stages:
    • Prefix and target detection.
    • Question-body generation with a COPYNET/pointer-generator model over BERT encodings, implemented in ALLENNLP.
    • Answer extraction with BERT fine-tuned on SQuAD, then further tuned on 5,004 aligned instances.
  • Full-pipeline test results:
    • UQA F1 of 83.69 (precision 80.79, recall 86.8).
    • LQA accuracy of 66.59.
    • Prefix accuracy of only 49.94.
  • In a manual check of 50 predicted QAs, 22 were correct and 2 more were valid but absent from the gold data. Other errors included ungrammatical-but-sensible and nonsensical outputs.

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

44 papers of our library cite it (of the 140673 whose bibliographies we read); 9 of them have a page here, 35 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2020.emnlp-main.224 · arXiv 2010.02815

@inproceedings{pyatkin2020qadiscourse,
  title = {{QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines}},
  author = {Pyatkin, Valentina and Klein, Ayal and Tsarfaty, Reut and Dagan, Ido},
  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2020},
  doi = {10.18653/v1/2020.emnlp-main.224},
  eprint = {2010.02815},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2020.emnlp-main.224}
}

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