Improving Candidate Generation for Low-resource Cross-lingual Entity Linking

Shuyan Zhou, Shruti Rijhwani, John Wieting, Jaime Carbonell, Graham Neubig


Abstract
Cross-lingual entity linking (XEL) is the task of finding referents in a target-language knowledge base (KB) for mentions extracted from source-language texts. The first step of (X)EL is candidate generation, which retrieves a list of plausible candidate entities from the target-language KB for each mention. Approaches based on resources from Wikipedia have proven successful in the realm of relatively high-resource languages, but these do not extend well to low-resource languages with few, if any, Wikipedia pages. Recently, transfer learning methods have been shown to reduce the demand for resources in the low-resource languages by utilizing resources in closely related languages, but the performance still lags far behind their high-resource counterparts. In this paper, we first assess the problems faced by current entity candidate generation methods for low-resource XEL, then propose three improvements that (1) reduce the disconnect between entity mentions and KB entries, and (2) improve the robustness of the model to low-resource scenarios. The methods are simple, but effective: We experiment with our approach on seven XEL datasets and find that they yield an average gain of 16.9% in Top-30 gold candidate recall, compared with state-of-the-art baselines. Our improved model also yields an average gain of 7.9% in in-KB accuracy of end-to-end XEL.1
Anthology ID:
2020.tacl-1.8
Volume:
Transactions of the Association for Computational Linguistics, Volume 8
Month:
Year:
2020
Address:
Cambridge, MA
Editors:
Mark Johnson, Brian Roark, Ani Nenkova
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
109–124
Language:
URL:
https://aclanthology.org/2020.tacl-1.8
DOI:
10.1162/tacl_a_00303
Bibkey:
Cite (ACL):
Shuyan Zhou, Shruti Rijhwani, John Wieting, Jaime Carbonell, and Graham Neubig. 2020. Improving Candidate Generation for Low-resource Cross-lingual Entity Linking. Transactions of the Association for Computational Linguistics, 8:109–124.
Cite (Informal):
Improving Candidate Generation for Low-resource Cross-lingual Entity Linking (Zhou et al., TACL 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/2020.tacl-1.8.pdf
Code
 shuyanzhou/pbel_plus