@inproceedings{mrini-etal-2022-detection,
title = "Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem",
author = "Mrini, Khalil and
Nie, Shaoliang and
Gu, Jiatao and
Wang, Sinong and
Sanjabi, Maziar and
Firooz, Hamed",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-acl.156/",
doi = "10.18653/v1/2022.findings-acl.156",
pages = "1972--1983",
abstract = "We propose an autoregressive entity linking model, that is trained with two auxiliary tasks, and learns to re-rank generated samples at inference time. Our proposed novelties address two weaknesses in the literature. First, a recent method proposes to learn mention detection and then entity candidate selection, but relies on predefined sets of candidates. We use encoder-decoder autoregressive entity linking in order to bypass this need, and propose to train mention detection as an auxiliary task instead. Second, previous work suggests that re-ranking could help correct prediction errors. We add a new, auxiliary task, match prediction, to learn re-ranking. Without the use of a knowledge base or candidate sets, our model sets a new state of the art in two benchmark datasets of entity linking: COMETA in the biomedical domain, and AIDA-CoNLL in the news domain. We show through ablation studies that each of the two auxiliary tasks increases performance, and that re-ranking is an important factor to the increase. Finally, our low-resource experimental results suggest that performance on the main task benefits from the knowledge learned by the auxiliary tasks, and not just from the additional training data."
}
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<abstract>We propose an autoregressive entity linking model, that is trained with two auxiliary tasks, and learns to re-rank generated samples at inference time. Our proposed novelties address two weaknesses in the literature. First, a recent method proposes to learn mention detection and then entity candidate selection, but relies on predefined sets of candidates. We use encoder-decoder autoregressive entity linking in order to bypass this need, and propose to train mention detection as an auxiliary task instead. Second, previous work suggests that re-ranking could help correct prediction errors. We add a new, auxiliary task, match prediction, to learn re-ranking. Without the use of a knowledge base or candidate sets, our model sets a new state of the art in two benchmark datasets of entity linking: COMETA in the biomedical domain, and AIDA-CoNLL in the news domain. We show through ablation studies that each of the two auxiliary tasks increases performance, and that re-ranking is an important factor to the increase. Finally, our low-resource experimental results suggest that performance on the main task benefits from the knowledge learned by the auxiliary tasks, and not just from the additional training data.</abstract>
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%0 Conference Proceedings
%T Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem
%A Mrini, Khalil
%A Nie, Shaoliang
%A Gu, Jiatao
%A Wang, Sinong
%A Sanjabi, Maziar
%A Firooz, Hamed
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Findings of the Association for Computational Linguistics: ACL 2022
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F mrini-etal-2022-detection
%X We propose an autoregressive entity linking model, that is trained with two auxiliary tasks, and learns to re-rank generated samples at inference time. Our proposed novelties address two weaknesses in the literature. First, a recent method proposes to learn mention detection and then entity candidate selection, but relies on predefined sets of candidates. We use encoder-decoder autoregressive entity linking in order to bypass this need, and propose to train mention detection as an auxiliary task instead. Second, previous work suggests that re-ranking could help correct prediction errors. We add a new, auxiliary task, match prediction, to learn re-ranking. Without the use of a knowledge base or candidate sets, our model sets a new state of the art in two benchmark datasets of entity linking: COMETA in the biomedical domain, and AIDA-CoNLL in the news domain. We show through ablation studies that each of the two auxiliary tasks increases performance, and that re-ranking is an important factor to the increase. Finally, our low-resource experimental results suggest that performance on the main task benefits from the knowledge learned by the auxiliary tasks, and not just from the additional training data.
%R 10.18653/v1/2022.findings-acl.156
%U https://aclanthology.org/2022.findings-acl.156/
%U https://doi.org/10.18653/v1/2022.findings-acl.156
%P 1972-1983
Markdown (Informal)
[Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem](https://aclanthology.org/2022.findings-acl.156/) (Mrini et al., Findings 2022)
ACL
- Khalil Mrini, Shaoliang Nie, Jiatao Gu, Sinong Wang, Maziar Sanjabi, and Hamed Firooz. 2022. Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem. In Findings of the Association for Computational Linguistics: ACL 2022, pages 1972–1983, Dublin, Ireland. Association for Computational Linguistics.