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Scaling Within Document Coreference to Long Texts

Summary

Neural end-to-end coreference systems in the tradition of Lee et al. (2017) consume a lot of memory and run slowly on long inputs such as books. They have two main bottlenecks. They build representations for every candidate span and span pair, and each mention must be compared with all earlier candidates. The authors approximate this architecture at the token level. They score mention boundaries and antecedent links with bilinear functions over encoder token embeddings rather than span vectors. To limit antecedent comparisons, they split documents into token windows, keep only k-nearest-neighbour token links, use low-rank factorisation of the token similarity matrix, and discard non-essential candidate tokens from memory. The model was tested on CoNLL-2012, Litbank and MedMentions. Its F1 is close to strong span-based baselines, while training and inference are faster and lighter. This makes neural coreference practical for very long documents on modest hardware. That matters for corpora of novels, blogs or other lengthy texts.

Key points

  • Evaluation datasets were CoNLL-2012 (OntoNotes), Litbank (literary texts that require singleton mentions) and MedMentions (biomedical texts).
  • The system processed a novel of about two million tokens in roughly 14 minutes using 12GB of GPU RAM. Earlier end-to-end systems reached only about 11k tokens even with up to 48GB.
  • On Litbank it ran up to 10x faster and used less memory than the memory-based incremental model of Toshniwal et al. (2020b).
  • In the ablation table, the full model reaches F1 of about 78.0 (CoNLL), 75.9 (Litbank) and 61.8 (MedMentions). On Litbank, removing the subtoken strategy and mention (pre)training lowers F1 to about 68.2. The CoNLL state of the art remains Wu et al. (2020), at about 83 F1, but that approach is too costly for long texts.
  • Code and trained models are released at github.com/raghavlite/Scalable-Coreference. The token-level bilinear scoring idea was developed concurrently by Kirstain et al. (2021).

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

13 papers of our library cite it (of the 140673 whose bibliographies we read); 6 of them have a page here, 7 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2021.findings-acl.343

@inproceedings{thirukovalluru2021scaling,
  title = {{Scaling Within Document Coreference to Long Texts}},
  author = {Thirukovalluru, Raghuveer and Monath, Nicholas and Shridhar, Kumar and Zaheer, Manzil and Sachan, Mrinmaya and McCallum, Andrew},
  booktitle = {Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021},
  year = {2021},
  doi = {10.18653/v1/2021.findings-acl.343},
  url = {https://doi.org/10.18653/v1/2021.findings-acl.343}
}

References

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