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        <title>Simultaneous Translation and Paraphrase for Language Education</title>
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    <name type="personal">
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    <abstract>We present the task of Simultaneous Translation and Paraphrasing for Language Education (STAPLE). Given a prompt in one language, the goal is to generate a diverse set of correct translations that language learners are likely to produce. This is motivated by the need to create and maintain large, high-quality sets of acceptable translations for exercises in a language-learning application, and synthesizes work spanning machine translation, MT evaluation, automatic paraphrasing, and language education technology. We developed a novel corpus with unique properties for five languages (Hungarian, Japanese, Korean, Portuguese, and Vietnamese), and report on the results of a shared task challenge which attracted 20 teams to solve the task. In our meta-analysis, we focus on three aspects of the resulting systems: external training corpus selection, model architecture and training decisions, and decoding and filtering strategies. We find that strong systems start with a large amount of generic training data, and then fine-tune with in-domain data, sampled according to our provided learner response frequencies.</abstract>
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%0 Conference Proceedings
%T Simultaneous Translation and Paraphrase for Language Education
%A Mayhew, Stephen
%A Bicknell, Klinton
%A Brust, Chris
%A McDowell, Bill
%A Monroe, Will
%A Settles, Burr
%S Proceedings of the Fourth Workshop on Neural Generation and Translation
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F mayhew-etal-2020-simultaneous
%X We present the task of Simultaneous Translation and Paraphrasing for Language Education (STAPLE). Given a prompt in one language, the goal is to generate a diverse set of correct translations that language learners are likely to produce. This is motivated by the need to create and maintain large, high-quality sets of acceptable translations for exercises in a language-learning application, and synthesizes work spanning machine translation, MT evaluation, automatic paraphrasing, and language education technology. We developed a novel corpus with unique properties for five languages (Hungarian, Japanese, Korean, Portuguese, and Vietnamese), and report on the results of a shared task challenge which attracted 20 teams to solve the task. In our meta-analysis, we focus on three aspects of the resulting systems: external training corpus selection, model architecture and training decisions, and decoding and filtering strategies. We find that strong systems start with a large amount of generic training data, and then fine-tune with in-domain data, sampled according to our provided learner response frequencies.
%R 10.18653/v1/2020.ngt-1.28
%U https://aclanthology.org/2020.ngt-1.28
%U https://doi.org/10.18653/v1/2020.ngt-1.28
%P 232-243

Markdown (Informal)

[Simultaneous Translation and Paraphrase for Language Education](https://aclanthology.org/2020.ngt-1.28) (Mayhew et al., NGT 2020)

• Simultaneous Translation and Paraphrase for Language Education (Mayhew et al., NGT 2020)

ACL

• Stephen Mayhew, Klinton Bicknell, Chris Brust, Bill McDowell, Will Monroe, and Burr Settles. 2020. Simultaneous Translation and Paraphrase for Language Education. In Proceedings of the Fourth Workshop on Neural Generation and Translation, pages 232–243, Online. Association for Computational Linguistics.