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        <title>Inria-ALMAnaCH at WMT 2022: Does Transcription Help Cross-Script Machine Translation?</title>
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    <name type="personal">
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    <name type="personal">
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    <name type="personal">
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    <name type="personal">
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    <abstract>This paper describes the Inria ALMAnaCH team submission to the WMT 2022 general translation shared task. Participating in the language directions cs,ru,uk→en and cs↔uk, we experiment with the use of a dedicated Latin-script transcription convention aimed at representing all Slavic languages involved in a way that maximises character- and word-level correspondences between them as well as with the English language. Our hypothesis was that bringing the source and target language closer could have a positive impact on machine translation results. We provide multiple comparisons, including bilingual and multilingual baselines, with and without transcription. Initial results indicate that the transcription strategy was not successful, resulting in lower results than baselines. We nevertheless submitted our multilingual, transcribed models as our primary systems, and in this paper provide some indications as to why we got these negative results.</abstract>
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%0 Conference Proceedings
%T Inria-ALMAnaCH at WMT 2022: Does Transcription Help Cross-Script Machine Translation?
%A Alabi, Jesujoba
%A Nishimwe, Lydia
%A Muller, Benjamin
%A Rey, Camille
%A Sagot, Benoît
%A Bawden, Rachel
%S Proceedings of the Seventh Conference on Machine Translation (WMT)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F alabi-etal-2022-inria
%X This paper describes the Inria ALMAnaCH team submission to the WMT 2022 general translation shared task. Participating in the language directions cs,ru,uk→en and cs↔uk, we experiment with the use of a dedicated Latin-script transcription convention aimed at representing all Slavic languages involved in a way that maximises character- and word-level correspondences between them as well as with the English language. Our hypothesis was that bringing the source and target language closer could have a positive impact on machine translation results. We provide multiple comparisons, including bilingual and multilingual baselines, with and without transcription. Initial results indicate that the transcription strategy was not successful, resulting in lower results than baselines. We nevertheless submitted our multilingual, transcribed models as our primary systems, and in this paper provide some indications as to why we got these negative results.
%U https://aclanthology.org/2022.wmt-1.15
%P 233-243

Markdown (Informal)

[Inria-ALMAnaCH at WMT 2022: Does Transcription Help Cross-Script Machine Translation?](https://aclanthology.org/2022.wmt-1.15) (Alabi et al., WMT 2022)

• Inria-ALMAnaCH at WMT 2022: Does Transcription Help Cross-Script Machine Translation? (Alabi et al., WMT 2022)

ACL

• Jesujoba Alabi, Lydia Nishimwe, Benjamin Muller, Camille Rey, Benoît Sagot, and Rachel Bawden. 2022. Inria-ALMAnaCH at WMT 2022: Does Transcription Help Cross-Script Machine Translation?. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 233–243, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.