@inproceedings{de-cao-etal-2021-highly,
title = "Highly Parallel Autoregressive Entity Linking with Discriminative Correction",
author = "De Cao, Nicola and
Aziz, Wilker and
Titov, Ivan",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.604/",
doi = "10.18653/v1/2021.emnlp-main.604",
pages = "7662--7669",
abstract = "Generative approaches have been recently shown to be effective for both Entity Disambiguation and Entity Linking (i.e., joint mention detection and disambiguation). However, the previously proposed autoregressive formulation for EL suffers from i) high computational cost due to a complex (deep) decoder, ii) non-parallelizable decoding that scales with the source sequence length, and iii) the need for training on a large amount of data. In this work, we propose a very efficient approach that parallelizes autoregressive linking across all potential mentions and relies on a shallow and efficient decoder. Moreover, we augment the generative objective with an extra discriminative component, i.e., a correction term which lets us directly optimize the generator`s ranking. When taken together, these techniques tackle all the above issues: our model is {\ensuremath{>}}70 times faster and more accurate than the previous generative method, outperforming state-of-the-art approaches on the standard English dataset AIDA-CoNLL. Source code available at \url{https://github.com/nicola-decao/efficient-autoregressive-EL}"
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<abstract>Generative approaches have been recently shown to be effective for both Entity Disambiguation and Entity Linking (i.e., joint mention detection and disambiguation). However, the previously proposed autoregressive formulation for EL suffers from i) high computational cost due to a complex (deep) decoder, ii) non-parallelizable decoding that scales with the source sequence length, and iii) the need for training on a large amount of data. In this work, we propose a very efficient approach that parallelizes autoregressive linking across all potential mentions and relies on a shallow and efficient decoder. Moreover, we augment the generative objective with an extra discriminative component, i.e., a correction term which lets us directly optimize the generator‘s ranking. When taken together, these techniques tackle all the above issues: our model is \ensuremath>70 times faster and more accurate than the previous generative method, outperforming state-of-the-art approaches on the standard English dataset AIDA-CoNLL. Source code available at https://github.com/nicola-decao/efficient-autoregressive-EL</abstract>
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%0 Conference Proceedings
%T Highly Parallel Autoregressive Entity Linking with Discriminative Correction
%A De Cao, Nicola
%A Aziz, Wilker
%A Titov, Ivan
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F de-cao-etal-2021-highly
%X Generative approaches have been recently shown to be effective for both Entity Disambiguation and Entity Linking (i.e., joint mention detection and disambiguation). However, the previously proposed autoregressive formulation for EL suffers from i) high computational cost due to a complex (deep) decoder, ii) non-parallelizable decoding that scales with the source sequence length, and iii) the need for training on a large amount of data. In this work, we propose a very efficient approach that parallelizes autoregressive linking across all potential mentions and relies on a shallow and efficient decoder. Moreover, we augment the generative objective with an extra discriminative component, i.e., a correction term which lets us directly optimize the generator‘s ranking. When taken together, these techniques tackle all the above issues: our model is \ensuremath>70 times faster and more accurate than the previous generative method, outperforming state-of-the-art approaches on the standard English dataset AIDA-CoNLL. Source code available at https://github.com/nicola-decao/efficient-autoregressive-EL
%R 10.18653/v1/2021.emnlp-main.604
%U https://aclanthology.org/2021.emnlp-main.604/
%U https://doi.org/10.18653/v1/2021.emnlp-main.604
%P 7662-7669
Markdown (Informal)
[Highly Parallel Autoregressive Entity Linking with Discriminative Correction](https://aclanthology.org/2021.emnlp-main.604/) (De Cao et al., EMNLP 2021)
ACL