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Neural Models for Reasoning over Multiple Mentions Using Coreference

Summary

Answering questions about text often requires combining facts about one entity that appear far apart, possibly across sentences or documents. Standard RNN layers tend to favour nearby context, so they handle this poorly, especially with little training data. The authors propose Coref-GRU (C-GRU), a GRU variant that takes coreference clusters from an external resolver as input. Each token's update draws on two sources: the previous token's hidden state and the state of its most recent coreferent antecedent. A learned scalar weight balances the two, so a pronoun, for example, can rely mostly on its antecedent. The layer can also be seen as a memory network whose read and write positions come from coreference links rather than being learned. Replacing the bidirectional GRUs in the Gated-Attention (GA) reader with C-GRUs improves results on bAbi-1K, Wikihop and LAMBADA. The gains are largest when training data is scarce, and the model sets a new state of the art on LAMBADA. This shows that even noisy coreference annotations can supply a useful inductive bias for reasoning over multiple mentions.

Key points

  • bAbi-1K (20 tasks):
    • GA w/ C-GRU reaches 0.870 average accuracy with 5 failed tasks.
    • GA w/ GRU reaches 0.764 with 10 failed tasks.
    • EntNets reach 0.704 (max) with 15 failed tasks.
    • The largest gains are on tasks 2, 3 and 16.
  • Ablations on bAbi:
    • Adding coreference as 1-hot input features to a plain GRU does not help.
    • Removing coreference links, or replacing them with random links, causes a sharp drop in accuracy.
    • Together these show that both the structural bias and the coreference signal matter.
  • Wikihop:
    • Coreference comes from the Berkeley Entity Resolution system (about 62% F1 on CoNLL 2011).
    • C-GRU clearly helps with 1K and 5K training examples, but with the full training set the two models perform comparably.
    • Both GA models far outperform the BiDAF result reported in prior work.
  • LAMBADA:
    • Coreference comes from Stanford CoreNLP (0.63 F1).
    • GA w/ C-GRU scores 0.5569 overall and 0.6888 on the context subset.
    • GA w/ GRU scores 0.5398 and 0.6677, and Chu et al. (2017) score 0.4900 overall.
  • Cost: time complexity matches a regular GRU, while memory grows to O(BTCd) because a hidden state is kept for each coreference cluster.

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: Coreference resolution

Cited by

43 papers of our library cite it (of the 140673 whose bibliographies we read); 9 of them have a page here, 34 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/n18-2007 · arXiv 1804.05922

@inproceedings{dhingra2018neural,
  title = {{Neural Models for Reasoning over Multiple Mentions Using Coreference}},
  author = {Dhingra, Bhuwan and Jin, Qiao and Yang, Zhilin and Cohen, William and Salakhutdinov, Ruslan},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
  year = {2018},
  doi = {10.18653/v1/n18-2007},
  eprint = {1804.05922},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/n18-2007}
}

References

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