@inproceedings{chan-etal-2020-empirical,
title = "An Empirical Study of Generation Order for Machine Translation",
author = "Chan, William and
Stern, Mitchell and
Kiros, Jamie and
Uszkoreit, Jakob",
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.464",
doi = "10.18653/v1/2020.emnlp-main.464",
pages = "5764--5773",
abstract = "In this work, we present an empirical study of generation order for machine translation. Building on recent advances in insertion-based modeling, we first introduce a soft order-reward framework that enables us to train models to follow arbitrary oracle generation policies. We then make use of this framework to explore a large variety of generation orders, including uninformed orders, location-based orders, frequency-based orders, content-based orders, and model-based orders. Curiously, we find that for the WMT{'}14 English $\to$ German and WMT{'}18 English $\to$ Chinese translation tasks, order does not have a substantial impact on output quality. Moreover, for English $\to$ German, we even discover that unintuitive orderings such as alphabetical and shortest-first can match the performance of a standard Transformer, suggesting that traditional left-to-right generation may not be necessary to achieve high performance.",
}
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<abstract>In this work, we present an empirical study of generation order for machine translation. Building on recent advances in insertion-based modeling, we first introduce a soft order-reward framework that enables us to train models to follow arbitrary oracle generation policies. We then make use of this framework to explore a large variety of generation orders, including uninformed orders, location-based orders, frequency-based orders, content-based orders, and model-based orders. Curiously, we find that for the WMT’14 English German and WMT’18 English Chinese translation tasks, order does not have a substantial impact on output quality. Moreover, for English German, we even discover that unintuitive orderings such as alphabetical and shortest-first can match the performance of a standard Transformer, suggesting that traditional left-to-right generation may not be necessary to achieve high performance.</abstract>
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%0 Conference Proceedings
%T An Empirical Study of Generation Order for Machine Translation
%A Chan, William
%A Stern, Mitchell
%A Kiros, Jamie
%A Uszkoreit, Jakob
%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 chan-etal-2020-empirical
%X In this work, we present an empirical study of generation order for machine translation. Building on recent advances in insertion-based modeling, we first introduce a soft order-reward framework that enables us to train models to follow arbitrary oracle generation policies. We then make use of this framework to explore a large variety of generation orders, including uninformed orders, location-based orders, frequency-based orders, content-based orders, and model-based orders. Curiously, we find that for the WMT’14 English German and WMT’18 English Chinese translation tasks, order does not have a substantial impact on output quality. Moreover, for English German, we even discover that unintuitive orderings such as alphabetical and shortest-first can match the performance of a standard Transformer, suggesting that traditional left-to-right generation may not be necessary to achieve high performance.
%R 10.18653/v1/2020.emnlp-main.464
%U https://aclanthology.org/2020.emnlp-main.464
%U https://doi.org/10.18653/v1/2020.emnlp-main.464
%P 5764-5773
Markdown (Informal)
[An Empirical Study of Generation Order for Machine Translation](https://aclanthology.org/2020.emnlp-main.464) (Chan et al., EMNLP 2020)
ACL