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        <title>Machine Translation Between High-resource Languages in a Language Documentation Setting</title>
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    <abstract>Language documentation encompasses translation, typically into the dominant high-resource language in the region where the target language is spoken. To make data accessible to a broader audience, additional translation into other high-resource languages might be needed. Working within a project documenting Kotiria, we explore the extent to which state-of-the-art machine translation (MT) systems can support this second translation – in our case from Portuguese to English. This translation task is challenging for multiple reasons: (1) the data is out-of-domain with respect to the MT system’s training data, (2) much of the data is conversational, (3) existing translations include non-standard and uncommon expressions, often reflecting properties of the documented language, and (4) the data includes borrowings from other regional languages. Despite these challenges, existing MT systems perform at a usable level, though there is still room for improvement. We then conduct a qualitative analysis and suggest ways to improve MT between high-resource languages in a language documentation setting.</abstract>
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%0 Conference Proceedings
%T Machine Translation Between High-resource Languages in a Language Documentation Setting
%A Kann, Katharina
%A Ebrahimi, Abteen
%A Stenzel, Kristine
%A Palmer, Alexis
%S Proceedings of the first workshop on NLP applications to field linguistics
%D 2022
%8 October
%I International Conference on Computational Linguistics
%C Gyeongju, Republic of Korea
%F kann-etal-2022-machine
%X Language documentation encompasses translation, typically into the dominant high-resource language in the region where the target language is spoken. To make data accessible to a broader audience, additional translation into other high-resource languages might be needed. Working within a project documenting Kotiria, we explore the extent to which state-of-the-art machine translation (MT) systems can support this second translation – in our case from Portuguese to English. This translation task is challenging for multiple reasons: (1) the data is out-of-domain with respect to the MT system’s training data, (2) much of the data is conversational, (3) existing translations include non-standard and uncommon expressions, often reflecting properties of the documented language, and (4) the data includes borrowings from other regional languages. Despite these challenges, existing MT systems perform at a usable level, though there is still room for improvement. We then conduct a qualitative analysis and suggest ways to improve MT between high-resource languages in a language documentation setting.
%U https://aclanthology.org/2022.fieldmatters-1.3
%P 26-33

Markdown (Informal)

[Machine Translation Between High-resource Languages in a Language Documentation Setting](https://aclanthology.org/2022.fieldmatters-1.3) (Kann et al., FieldMatters 2022)

• Machine Translation Between High-resource Languages in a Language Documentation Setting (Kann et al., FieldMatters 2022)

ACL

• Katharina Kann, Abteen Ebrahimi, Kristine Stenzel, and Alexis Palmer. 2022. Machine Translation Between High-resource Languages in a Language Documentation Setting. In Proceedings of the first workshop on NLP applications to field linguistics, pages 26–33, Gyeongju, Republic of Korea. International Conference on Computational Linguistics.