@inproceedings{li-etal-2020-efficient,
title = "Efficient One-Pass End-to-End Entity Linking for Questions",
author = "Li, Belinda Z. and
Min, Sewon and
Iyer, Srinivasan and
Mehdad, Yashar and
Yih, Wen-tau",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.522",
doi = "10.18653/v1/2020.emnlp-main.522",
pages = "6433--6441",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Efficient One-Pass End-to-End Entity Linking for Questions
%A Li, Belinda Z.
%A Min, Sewon
%A Iyer, Srinivasan
%A Mehdad, Yashar
%A Yih, Wen-tau
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F li-etal-2020-efficient
%X 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.
%R 10.18653/v1/2020.emnlp-main.522
%U https://aclanthology.org/2020.emnlp-main.522
%U https://doi.org/10.18653/v1/2020.emnlp-main.522
%P 6433-6441
Markdown (Informal)
[Efficient One-Pass End-to-End Entity Linking for Questions](https://aclanthology.org/2020.emnlp-main.522) (Li et al., EMNLP 2020)
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.