Name
Abstract | ||
|---|---|---|
We propose an entity-centric neural cross-lingual 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. |
Year | Venue | DocType |
|---|---|---|
2018 | PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2 | Journal |
Volume | ISSN | Citations |
abs/1806.10201 | ACL 2018 | 4 |
PageRank | References | Authors |
0.36 | 21 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gourab Kundu | 1 | 68 | 6.35 |
| Avirup Sil | 2 | 131 | 13.85 |
| Radu Florian | 3 | 924 | 91.44 |
| wael hamza | 4 | 198 | 15.84 |