Efficient One-Pass End-to-End Entity Linking for Questions
Belinda Z. Li, Sewon Min, Srinivasan Iyer, Yashar Mehdad, Wen-tau Yih
Abstract
We present ELQ, a fast end-to-end entity linking model for questions, which uses a biencoder to jointly perform mention detection and linking in one pass. Evaluated on WebQSP and GraphQuestions with extended annotations that cover multiple entities per question, ELQ outperforms the previous state of the art by a large margin of +12.7% and +19.6% F1, respectively. With a very fast inference time (1.57 examples/s on a single CPU), ELQ can be useful for downstream question answering systems. In a proof-of-concept experiment, we demonstrate that using ELQ significantly improves the downstream QA performance of GraphRetriever.- Anthology ID:
- 2020.emnlp-main.522
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6433–6441
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.522
- DOI:
- 10.18653/v1/2020.emnlp-main.522
- Cite (ACL):
- Belinda Z. Li, Sewon Min, Srinivasan Iyer, Yashar Mehdad, and Wen-tau Yih. 2020. Efficient One-Pass End-to-End Entity Linking for Questions. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6433–6441, Online. Association for Computational Linguistics.
- Cite (Informal):
- Efficient One-Pass End-to-End Entity Linking for Questions (Li et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2020.emnlp-main.522.pdf
- Code
- facebookresearch/BLINK + additional community code
- Data
- GraphQuestions, Natural Questions, TriviaQA, WebQuestions