Neural Cross-Lingual Coreference Resolution And Its Application To Entity Linking

Gourab Kundu, Avi Sil, Radu Florian, Wael Hamza


Abstract
We propose an entity-centric neural crosslingual coreference model that builds on multi-lingual embeddings and language independent features. We perform both intrinsic and extrinsic evaluations of our model. In the intrinsic evaluation, we show that our model, when trained on English and tested on Chinese and Spanish, achieves competitive results to the models trained directly on Chinese and Spanish respectively. In the extrinsic evaluation, we show that our English model helps achieve superior entity linking accuracy on Chinese and Spanish test sets than the top 2015 TAC system without using any annotated data from Chinese or Spanish.
Anthology ID:
P18-2063
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
395–400
Language:
URL:
https://aclanthology.org/P18-2063
DOI:
10.18653/v1/P18-2063
Bibkey:
Cite (ACL):
Gourab Kundu, Avi Sil, Radu Florian, and Wael Hamza. 2018. Neural Cross-Lingual Coreference Resolution And Its Application To Entity Linking. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 395–400, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Neural Cross-Lingual Coreference Resolution And Its Application To Entity Linking (Kundu et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-2063.pdf
Video:
 https://aclanthology.org/P18-2063.mp4