@inproceedings{haviv-etal-2021-latent,
title = "Can Latent Alignments Improve Autoregressive Machine Translation?",
author = "Haviv, Adi and
Vassertail, Lior and
Levy, Omer",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.209",
doi = "10.18653/v1/2021.naacl-main.209",
pages = "2637--2641",
abstract = "Latent alignment objectives such as CTC and AXE significantly improve non-autoregressive machine translation models. Can they improve autoregressive models as well? We explore the possibility of training autoregressive machine translation models with latent alignment objectives, and observe that, in practice, this approach results in degenerate models. We provide a theoretical explanation for these empirical results, and prove that latent alignment objectives are incompatible with teacher forcing.",
}
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<abstract>Latent alignment objectives such as CTC and AXE significantly improve non-autoregressive machine translation models. Can they improve autoregressive models as well? We explore the possibility of training autoregressive machine translation models with latent alignment objectives, and observe that, in practice, this approach results in degenerate models. We provide a theoretical explanation for these empirical results, and prove that latent alignment objectives are incompatible with teacher forcing.</abstract>
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%0 Conference Proceedings
%T Can Latent Alignments Improve Autoregressive Machine Translation?
%A Haviv, Adi
%A Vassertail, Lior
%A Levy, Omer
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F haviv-etal-2021-latent
%X Latent alignment objectives such as CTC and AXE significantly improve non-autoregressive machine translation models. Can they improve autoregressive models as well? We explore the possibility of training autoregressive machine translation models with latent alignment objectives, and observe that, in practice, this approach results in degenerate models. We provide a theoretical explanation for these empirical results, and prove that latent alignment objectives are incompatible with teacher forcing.
%R 10.18653/v1/2021.naacl-main.209
%U https://aclanthology.org/2021.naacl-main.209
%U https://doi.org/10.18653/v1/2021.naacl-main.209
%P 2637-2641
Markdown (Informal)
[Can Latent Alignments Improve Autoregressive Machine Translation?](https://aclanthology.org/2021.naacl-main.209) (Haviv et al., NAACL 2021)
ACL
- Adi Haviv, Lior Vassertail, and Omer Levy. 2021. Can Latent Alignments Improve Autoregressive Machine Translation?. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2637–2641, Online. Association for Computational Linguistics.