Iranzo-Sánchez, Javier ; Civera, Jorge ; Juan, Alfons From Simultaneous to Streaming Machine Translation by Leveraging Streaming History Inproceedings Proc. 60th Annual Meeting of the Association for Computational Linguistics Vol. 1: Long Papers (ACL 2022), pp. 6972–6985, Dublin (Ireland), 2022. Abstract | Links | BibTeX | Tags: simultaneous machine translation, streaming machine translation @inproceedings{Iranzo-Sánchez2022,
title = {From Simultaneous to Streaming Machine Translation by Leveraging Streaming History},
author = {Iranzo-Sánchez, Javier and Civera, Jorge and Juan, Alfons},
url = {https://arxiv.org/abs/2203.02459
https://github.com/jairsan/Speech_Translation_Segmenter},
doi = {10.18653/v1/2022.acl-long.480},
year = {2022},
date = {2022-01-01},
booktitle = {Proc. 60th Annual Meeting of the Association for Computational Linguistics Vol. 1: Long Papers (ACL 2022)},
pages = {6972--6985},
address = {Dublin (Ireland)},
abstract = {Simultaneous Machine Translation is the task of incrementally translating an input sentence before it is fully available. Currently, simultaneous translation is carried out by translating each sentence independently of the previously translated text. More generally, Streaming MT can be understood as an extension of Simultaneous MT to the incremental translation of a continuous input text stream. In this work, a state-of-the-art simultaneous sentence-level MT system is extended to the streaming setup by leveraging the streaming history. Extensive empirical results are reported on IWSLT Translation Tasks, showing that leveraging the streaming history leads to significant quality gains. In particular, the proposed system proves to compare favorably to the best performing systems.},
keywords = {simultaneous machine translation, streaming machine translation},
pubstate = {published},
tppubtype = {inproceedings}
}
Simultaneous Machine Translation is the task of incrementally translating an input sentence before it is fully available. Currently, simultaneous translation is carried out by translating each sentence independently of the previously translated text. More generally, Streaming MT can be understood as an extension of Simultaneous MT to the incremental translation of a continuous input text stream. In this work, a state-of-the-art simultaneous sentence-level MT system is extended to the streaming setup by leveraging the streaming history. Extensive empirical results are reported on IWSLT Translation Tasks, showing that leveraging the streaming history leads to significant quality gains. In particular, the proposed system proves to compare favorably to the best performing systems. |
Javier Iranzo-Sánchez Jorge Civera, Alfons Juan Stream-level Latency Evaluation for Simultaneous Machine Translation Inproceedings Findings of the ACL: EMNLP 2021, pp. 664–670, Punta Cana (Dominican Republic), 2021. Abstract | Links | BibTeX | Tags: latency, simultaneous machine translation, stream-level evaluation, streaming @inproceedings{Iranzo-Sánchez2021b,
title = {Stream-level Latency Evaluation for Simultaneous Machine Translation},
author = {Javier Iranzo-Sánchez, Jorge Civera, Alfons Juan},
url = {https://arxiv.org/abs/2104.08817
https://github.com/jairsan/Stream-level_Latency_Evaluation_for_Simultaneous_Machine_Translation},
doi = {10.18653/v1/2021.findings-emnlp.58},
year = {2021},
date = {2021-01-01},
booktitle = {Findings of the ACL: EMNLP 2021},
pages = {664--670},
address = {Punta Cana (Dominican Republic)},
abstract = {Simultaneous machine translation has recently gained traction thanks to significant quality improvements and the advent of streaming applications. Simultaneous translation systems need to find a trade-off between translation quality and response time, and with this purpose multiple latency measures have been proposed. However, latency evaluations for simultaneous translation are estimated at the sentence level, not taking into account the sequential nature of a streaming scenario. Indeed, these sentence-level latency measures are not well suited for continuous stream translation, resulting in figures that are not coherent with the simultaneous translation policy of the system being assessed. This work proposes a stream level adaptation of the current latency measures based on a re-segmentation approach applied to the output translation, that is successfully evaluated on streaming conditions for a reference IWSLT task.},
keywords = {latency, simultaneous machine translation, stream-level evaluation, streaming},
pubstate = {published},
tppubtype = {inproceedings}
}
Simultaneous machine translation has recently gained traction thanks to significant quality improvements and the advent of streaming applications. Simultaneous translation systems need to find a trade-off between translation quality and response time, and with this purpose multiple latency measures have been proposed. However, latency evaluations for simultaneous translation are estimated at the sentence level, not taking into account the sequential nature of a streaming scenario. Indeed, these sentence-level latency measures are not well suited for continuous stream translation, resulting in figures that are not coherent with the simultaneous translation policy of the system being assessed. This work proposes a stream level adaptation of the current latency measures based on a re-segmentation approach applied to the output translation, that is successfully evaluated on streaming conditions for a reference IWSLT task. |
