Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities.
Example:
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I voted for Obama because he was most aligned with my values", she said.
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"I", "my", and "she" belong to the same cluster and "Obama" and "he" belong to the same cluster.
Experiments are conducted on the data of the CoNLL-2012 shared task, which uses OntoNotes coreference annotations. Papers report the precision, recall, and F1 of the MUC, B3, and CEAFφ4 metrics using the official CoNLL-2012 evaluation scripts. The main evaluation metric is the average F1 of the three metrics.
| Model | Avg F1 | Paper / Source | Code |
|---|---|---|---|
| Joshi et al. (2019)1 | 76.9 | BERT for Coreference Resolution: Baselines and Analysis | Official |
| Kantor and Globerson (2019) | 76.6 | Coreference Resolution with Entity Equalization | Official |
| Fei et al. (2019) | 73.8 | End-to-end Deep Reinforcement Learning Based Coreference Resolution | |
| (Lee et al., 2017)+ELMo (Peters et al., 2018)+coarse-to-fine & second-order inference (Lee et al., 2018) | 73.0 | Higher-order Coreference Resolution with Coarse-to-fine Inference | Official |
| (Lee et al., 2017)+ELMo (Peters et al., 2018) | 70.4 | Deep contextualized word representatIions | |
| Lee et al. (2017) | 67.2 | End-to-end Neural Coreference Resolution |
[1] Joshi et al. (2019): (Lee et al., 2017)+coarse-to-fine & second-order inference (Lee et al., 2018)+BERT (Devlin et al., 2019)