Bridging Anaphora Resolution as Question Answering

What it is about

The paper treats bridging anaphora resolution as question answering. Each anaphor is rephrased as a question such as "residents of what?" from its head and premodifiers, and a BERT-based span model (BARQA) finds the antecedent in the preceding context. The model needs no gold mentions. The authors also generate about 2.8 million "quasi-bridging" training pairs from parsed Gigaword: an "X of Y" or possessive NP gives an anaphor X and an antecedent Y in an earlier sentence. On a random sample of 100 pairs, 25% are rated fully acceptable and 37% acceptable but unnatural, and 38% are unacceptable. On ISNotes, the best setting (quasi-bridging pre-training, then BASHI fine-tuning) reaches 47.21% lenient accuracy. Using gold mentions for candidates, the strict accuracy is 50.08%, the new state of the art at the time.

Ideas

• I1. Use the question form as a deterministic step: build "a of what?" from the UD subtree of a definite NP, with the post-modifiers removed, and then search the earlier sentences for a head noun that can fill it. Record the rule that fired.
• I2. Generate quasi-bridging pairs from the UD trees Mova already has: take nmod "of" and nmod:poss NPs whose two heads share no other NP nodes, then find the earlier sentence that contains Y. Check a sample of 100 by hand with the paper's 0/1/2 scale before using any pair.
• I3. Keep the constraint that the antecedent precedes the anaphor, and the pruning to the top 20 spans of at most 5 words, which drops function words. Test each constraint on its own with a negative control.
• I4. Report lenient and strict accuracy separately, as the paper does, so the effect of the head-match rule is visible.

For Mova

The chapter is 35. Reference and coreference. The code is the coref crate, mainly coref/src/sieve.rs and coref/src/mention.rs, with the UD parse from en/src/parse.rs. The measure is accuracy on bridging anaphors with the lenient and strict splits. ISNotes and BASHI are used only for evaluation, and their licences should be checked first. The BERT model is not for Mova. The question rewriting and the quasi-bridging data generation are the parts worth copying.

Topics: Coreference resolution

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/2020.acl-main.132 · arXiv 2004.07898

@inproceedings{hou2020bridging,
  title = {{Bridging Anaphora Resolution as Question Answering}},
  author = {Hou, Yufang},
  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  year = {2020},
  doi = {10.18653/v1/2020.acl-main.132},
  eprint = {2004.07898},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2020.acl-main.132}
}

References

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