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        <title>Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning</title>
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    <abstract>We present a novel approach to efficiently learn a simultaneous translation model with coupled programmer-interpreter policies. First, we present an algorithmic oracle to produce oracle READ/WRITE actions for training bilingual sentence-pairs using the notion of word alignments. This oracle actions are designed to capture enough information from the partial input before writing the output. Next, we perform a coupled scheduled sampling to effectively mitigate the exposure bias when learning both policies jointly with imitation learning. Experiments on six language-pairs show our method outperforms strong baselines in terms of translation quality quality while keeping the delay low.</abstract>
    <identifier type="citekey">arthur-etal-2021-learning</identifier>
    <identifier type="doi">10.18653/v1/2021.eacl-main.233</identifier>
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        <date>2021-04</date>
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%0 Conference Proceedings
%T Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning
%A Arthur, Philip
%A Cohn, Trevor
%A Haffari, Gholamreza
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F arthur-etal-2021-learning
%X We present a novel approach to efficiently learn a simultaneous translation model with coupled programmer-interpreter policies. First, we present an algorithmic oracle to produce oracle READ/WRITE actions for training bilingual sentence-pairs using the notion of word alignments. This oracle actions are designed to capture enough information from the partial input before writing the output. Next, we perform a coupled scheduled sampling to effectively mitigate the exposure bias when learning both policies jointly with imitation learning. Experiments on six language-pairs show our method outperforms strong baselines in terms of translation quality quality while keeping the delay low.
%R 10.18653/v1/2021.eacl-main.233
%U https://aclanthology.org/2021.eacl-main.233
%U https://doi.org/10.18653/v1/2021.eacl-main.233
%P 2709-2719

Markdown (Informal)

[Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning](https://aclanthology.org/2021.eacl-main.233) (Arthur et al., EACL 2021)

• Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning (Arthur et al., EACL 2021)

ACL

• Philip Arthur, Trevor Cohn, and Gholamreza Haffari. 2021. Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2709–2719, Online. Association for Computational Linguistics.