Abstract
We propose a triad-based neural network system that generates affinity scores between entity mentions for coreference resolution. The system simultaneously accepts three mentions as input, taking mutual dependency and logical constraints of all three mentions into account, and thus makes more accurate predictions than the traditional pairwise approach. Depending on system choices, the affinity scores can be further used in clustering or mention ranking. Our experiments show that a standard hierarchical clustering using the scores produces state-of-art results with MUC and B 3 metrics on the English portion of CoNLL 2012 Shared Task. The model does not rely on many handcrafted features and is easy to train and use. The triads can also be easily extended to polyads of higher orders. To our knowledge, this is the first neural network system to model mutual dependency of more than two members at mention level.- Anthology ID:
- C18-1004
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Editors:
- Emily M. Bender, Leon Derczynski, Pierre Isabelle
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 35–43
- Language:
- URL:
- https://aclanthology.org/C18-1004
- DOI:
- Cite (ACL):
- Yuanliang Meng and Anna Rumshisky. 2018. Triad-based Neural Network for Coreference Resolution. In Proceedings of the 27th International Conference on Computational Linguistics, pages 35–43, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Triad-based Neural Network for Coreference Resolution (Meng & Rumshisky, COLING 2018)
- PDF:
- https://preview.aclanthology.org/naacl24-info/C18-1004.pdf
- Code
- text-machine-lab/entity-coref
- Data
- CoNLL-2012