@inproceedings{leblond-etal-2021-machine,
title = "Machine Translation Decoding beyond Beam Search",
author = "Leblond, R{\'e}mi and
Alayrac, Jean-Baptiste and
Sifre, Laurent and
Pislar, Miruna and
Jean-Baptiste, Lespiau and
Antonoglou, Ioannis and
Simonyan, Karen and
Vinyals, Oriol",
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.662",
doi = "10.18653/v1/2021.emnlp-main.662",
pages = "8410--8434",
abstract = "Beam search is the go-to method for decoding auto-regressive machine translation models. While it yields consistent improvements in terms of BLEU, it is only concerned with finding outputs with high model likelihood, and is thus agnostic to whatever end metric or score practitioners care about. Our aim is to establish whether beam search can be replaced by a more powerful metric-driven search technique. To this end, we explore numerous decoding algorithms, including some which rely on a value function parameterised by a neural network, and report results on a variety of metrics. Notably, we introduce a Monte-Carlo Tree Search (MCTS) based method and showcase its competitiveness. We provide a blueprint for how to use MCTS fruitfully in language applications, which opens promising future directions. We find that which algorithm is best heavily depends on the characteristics of the goal metric; we believe that our extensive experiments and analysis will inform further research in this area.",
}
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<abstract>Beam search is the go-to method for decoding auto-regressive machine translation models. While it yields consistent improvements in terms of BLEU, it is only concerned with finding outputs with high model likelihood, and is thus agnostic to whatever end metric or score practitioners care about. Our aim is to establish whether beam search can be replaced by a more powerful metric-driven search technique. To this end, we explore numerous decoding algorithms, including some which rely on a value function parameterised by a neural network, and report results on a variety of metrics. Notably, we introduce a Monte-Carlo Tree Search (MCTS) based method and showcase its competitiveness. We provide a blueprint for how to use MCTS fruitfully in language applications, which opens promising future directions. We find that which algorithm is best heavily depends on the characteristics of the goal metric; we believe that our extensive experiments and analysis will inform further research in this area.</abstract>
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%0 Conference Proceedings
%T Machine Translation Decoding beyond Beam Search
%A Leblond, Rémi
%A Alayrac, Jean-Baptiste
%A Sifre, Laurent
%A Pislar, Miruna
%A Jean-Baptiste, Lespiau
%A Antonoglou, Ioannis
%A Simonyan, Karen
%A Vinyals, Oriol
%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 leblond-etal-2021-machine
%X Beam search is the go-to method for decoding auto-regressive machine translation models. While it yields consistent improvements in terms of BLEU, it is only concerned with finding outputs with high model likelihood, and is thus agnostic to whatever end metric or score practitioners care about. Our aim is to establish whether beam search can be replaced by a more powerful metric-driven search technique. To this end, we explore numerous decoding algorithms, including some which rely on a value function parameterised by a neural network, and report results on a variety of metrics. Notably, we introduce a Monte-Carlo Tree Search (MCTS) based method and showcase its competitiveness. We provide a blueprint for how to use MCTS fruitfully in language applications, which opens promising future directions. We find that which algorithm is best heavily depends on the characteristics of the goal metric; we believe that our extensive experiments and analysis will inform further research in this area.
%R 10.18653/v1/2021.emnlp-main.662
%U https://aclanthology.org/2021.emnlp-main.662
%U https://doi.org/10.18653/v1/2021.emnlp-main.662
%P 8410-8434
Markdown (Informal)
[Machine Translation Decoding beyond Beam Search](https://aclanthology.org/2021.emnlp-main.662) (Leblond et al., EMNLP 2021)
ACL
- Rémi Leblond, Jean-Baptiste Alayrac, Laurent Sifre, Miruna Pislar, Lespiau Jean-Baptiste, Ioannis Antonoglou, Karen Simonyan, and Oriol Vinyals. 2021. Machine Translation Decoding beyond Beam Search. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8410–8434, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.