BERT for Coreference Resolution: Baselines and Analysis
2019-08-24IJCNLP 2019Code Available0· sign in to hype
Mandar Joshi, Omer Levy, Daniel S. Weld, Luke Zettlemoyer
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/mandarjoshi90/corefOfficialIn papertf★ 0
- github.com/wooseok-AI/Korean_e2e_CR_BERTtf★ 0
Abstract
We apply BERT to coreference resolution, achieving strong improvements on the OntoNotes (+3.9 F1) and GAP (+11.5 F1) benchmarks. A qualitative analysis of model predictions indicates that, compared to ELMo and BERT-base, BERT-large is particularly better at distinguishing between related but distinct entities (e.g., President and CEO). However, there is still room for improvement in modeling document-level context, conversations, and mention paraphrasing. Our code and models are publicly available.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| CoNLL-2012 | c2f-coref + BERT-large | Avg F1 | 76.9 | — | Unverified |
| OntoNotes | BERT-large | F1 | 76.9 | — | Unverified |
| OntoNotes | BERT-base | F1 | 73.9 | — | Unverified |