Abstract
Cross-lingual Entity Linking (XEL) aims to ground entity mentions written in any language to an English Knowledge Base (KB), such as Wikipedia. XEL for most languages is challenging, owing to limited availability of resources as supervision. We address this challenge by developing the first XEL approach that combines supervision from multiple languages jointly. This enables our approach to: (a) augment the limited supervision in the target language with additional supervision from a high-resource language (like English), and (b) train a single entity linking model for multiple languages, improving upon individually trained models for each language. Extensive evaluation on three benchmark datasets across 8 languages shows that our approach significantly improves over the current state-of-the-art. We also provide analyses in two limited resource settings: (a) zero-shot setting, when no supervision in the target language is available, and in (b) low-resource setting, when some supervision in the target language is available. Our analysis provides insights into the limitations of zero-shot XEL approaches in realistic scenarios, and shows the value of joint supervision in low-resource settings.- Anthology ID:
- D18-1270
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2486–2495
- Language:
- URL:
- https://aclanthology.org/D18-1270
- DOI:
- 10.18653/v1/D18-1270
- Cite (ACL):
- Shyam Upadhyay, Nitish Gupta, and Dan Roth. 2018. Joint Multilingual Supervision for Cross-lingual Entity Linking. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2486–2495, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Joint Multilingual Supervision for Cross-lingual Entity Linking (Upadhyay et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/D18-1270.pdf
- Code
- shyamupa/xelms