Iranzo-Sánchez, Javier; Iranzo-Sánchez, Jorge; Giménez, Adrià; Civera, Jorge; Juan, Alfons Segmentation-Free Streaming Machine Translation Journal Article Transactions of the Association for Computational Linguistics, 12 , pp. 1104-1121, 2024, (also accepted for presentation at ACL 2024). Abstract | Links | BibTeX | Tags: segmentation-free, streaming machine translation @article{Juan2024,
title = {Segmentation-Free Streaming Machine Translation},
author = {Javier Iranzo-Sánchez AND Jorge Iranzo-Sánchez AND Adrià Giménez AND Jorge Civera AND Alfons Juan},
url = {https://paperswithcode.com/paper/segmentation-free-streaming-machine
https://github.com/jairsan/Segmentation-Free_Streaming_Machine_Translation
https://arxiv.org/abs/2309.14823
https://2024.aclweb.org/program/tacl_papers/
https://www.mllp.upv.es/wp-content/uploads/2024/09/tacl_segfree_poster.pdf},
doi = {10.1162/tacl_a_00691},
year = {2024},
date = {2024-01-01},
journal = {Transactions of the Association for Computational Linguistics},
volume = {12},
pages = {1104-1121},
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.},
note = {also accepted for presentation at ACL 2024},
keywords = {segmentation-free, streaming machine translation},
pubstate = {published},
tppubtype = {article}
}
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. |
