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arXiv · 2309.14823

Segmentation-Free Streaming Machine Translation

Javier Iranzo-Sánchez·Jorge Iranzo-Sánchez·Adrià Giménez·Jorge Civera·Alfons Juan
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Abstract

Streaming Machine Translation (MT) is the task of translating an unbounded input text stream in real-time. The traditional cascade approach, which combines an Automatic Speech Recognition (ASR) and an MT system, relies on an intermediate segmentation step which splits the transcription stream into sentence-like units. However, the incorporation of a hard segmentation constrains the MT system and is a source of errors. This paper proposes a Segmentation-Free framework that enables the model to translate an unsegmented source stream by delaying the segmentation decision until the translation has been generated. Extensive experiments show how the proposed Segmentation-Free framework has better quality-latency trade-off than competing approaches that use an independent segmentation model. Software, data and models will be released upon paper acceptance.

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Javier Iranzo-Sánchez, Jorge Iranzo-Sánchez, Adrià Giménez, Jorge Civera, Alfons Juan. 2023-09-26. Segmentation-Free Streaming Machine Translation. https://arxiv.org/abs/2309.14823

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