Inria-ALMAnaCH at the WMT 2022 shared task: Does Transcription Help Cross-Script Machine Translation? - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2022

Inria-ALMAnaCH at the WMT 2022 shared task: Does Transcription Help Cross-Script Machine Translation?

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.
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Dates and versions

hal-03836180 , version 1 (01-11-2022)

Identifiers

  • HAL Id : hal-03836180 , version 1

Cite

Jesujoba O Alabi, Lydia Nishimwe, Benjamin Muller, Camille Rey, Benoît Sagot, et al.. Inria-ALMAnaCH at the WMT 2022 shared task: Does Transcription Help Cross-Script Machine Translation?. EMNLP 2022 - Seventh Conference on Machine Translation (WMT22 - Workshop on Statistical Machine Translation), Dec 2022, Abu Dhabi, United Arab Emirates. ⟨hal-03836180⟩
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