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        <title>Selecting Machine-Translated Data for Quick Bootstrapping of a Natural Language Understanding System</title>
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        <namePart type="given">Judith</namePart>
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        <namePart type="given">Penny</namePart>
        <namePart type="family">Karanasou</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)</title>
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            <namePart type="given">Srinivas</namePart>
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            <namePart type="given">Yunyao</namePart>
            <namePart type="family">Li</namePart>
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    <abstract>This paper investigates the use of Machine Translation (MT) to bootstrap a Natural Language Understanding (NLU) system for a new language for the use case of a large-scale voice-controlled device. The goal is to decrease the cost and time needed to get an annotated corpus for the new language, while still having a large enough coverage of user requests. Different methods of filtering MT data in order to keep utterances that improve NLU performance and language-specific post-processing methods are investigated. These methods are tested in a large-scale NLU task with translating around 10 millions training utterances from English to German. The results show a large improvement for using MT data over a grammar-based and over an in-house data collection baseline, while reducing the manual effort greatly. Both filtering and post-processing approaches improve results further.</abstract>
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    <identifier type="doi">10.18653/v1/N18-3017</identifier>
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        <date>2018-06</date>
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%0 Conference Proceedings
%T Selecting Machine-Translated Data for Quick Bootstrapping of a Natural Language Understanding System
%A Gaspers, Judith
%A Karanasou, Penny
%A Chatterjee, Rajen
%Y Bangalore, Srinivas
%Y Chu-Carroll, Jennifer
%Y Li, Yunyao
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans - Louisiana
%F gaspers-etal-2018-selecting
%X This paper investigates the use of Machine Translation (MT) to bootstrap a Natural Language Understanding (NLU) system for a new language for the use case of a large-scale voice-controlled device. The goal is to decrease the cost and time needed to get an annotated corpus for the new language, while still having a large enough coverage of user requests. Different methods of filtering MT data in order to keep utterances that improve NLU performance and language-specific post-processing methods are investigated. These methods are tested in a large-scale NLU task with translating around 10 millions training utterances from English to German. The results show a large improvement for using MT data over a grammar-based and over an in-house data collection baseline, while reducing the manual effort greatly. Both filtering and post-processing approaches improve results further.
%R 10.18653/v1/N18-3017
%U https://aclanthology.org/N18-3017
%U https://doi.org/10.18653/v1/N18-3017
%P 137-144

Markdown (Informal)

[Selecting Machine-Translated Data for Quick Bootstrapping of a Natural Language Understanding System](https://aclanthology.org/N18-3017) (Gaspers et al., NAACL 2018)

• Selecting Machine-Translated Data for Quick Bootstrapping of a Natural Language Understanding System (Gaspers et al., NAACL 2018)

ACL

• Judith Gaspers, Penny Karanasou, and Rajen Chatterjee. 2018. Selecting Machine-Translated Data for Quick Bootstrapping of a Natural Language Understanding System. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers), pages 137–144, New Orleans - Louisiana. Association for Computational Linguistics.