Abstract
Coreference evaluation metrics are hard to optimize directly as they are non-differentiable functions, not easily decomposable into elementary decisions. Consequently, most approaches optimize objectives only indirectly related to the end goal, resulting in suboptimal performance. Instead, we propose a differentiable relaxation that lends itself to gradient-based optimisation, thus bypassing the need for reinforcement learning or heuristic modification of cross-entropy. We show that by modifying the training objective of a competitive neural coreference system, we obtain a substantial gain in performance. This suggests that our approach can be regarded as a viable alternative to using reinforcement learning or more computationally expensive imitation learning.- Anthology ID:
- K17-1039
- Volume:
- Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Roger Levy, Lucia Specia
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 390–399
- Language:
- URL:
- https://aclanthology.org/K17-1039
- DOI:
- 10.18653/v1/K17-1039
- Cite (ACL):
- Phong Le and Ivan Titov. 2017. Optimizing Differentiable Relaxations of Coreference Evaluation Metrics. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 390–399, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Optimizing Differentiable Relaxations of Coreference Evaluation Metrics (Le & Titov, CoNLL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/K17-1039.pdf
- Code
- lephong/diffmetric_coref
- Data
- CoNLL-2012