Triad-based Neural Network for Coreference Resolution

Yuanliang Meng, Anna Rumshisky


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:
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/C18-1004.pdf
Code
 text-machine-lab/entity-coref
Data
CoNLL-2012