Iranzo-Sánchez, J.; Civera Saiz, J.; Juan, A. (2022). From Simultaneous to Streaming Machine Translation by Leveraging Streaming History. Association for Computational Linguistics. 6972-6985. https://doi.org/10.18653/v1/2022.acl-long.480
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/202322
Título:
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From Simultaneous to Streaming Machine Translation by Leveraging Streaming History
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Autor:
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Iranzo-Sánchez, Javier
Civera Saiz, Jorge
Juan, Alfons
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Entidad UPV:
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Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica
Universitat Politècnica de València. Instituto Universitario Valenciano de Investigación en Inteligencia Artificial - Institut Universitari Valencià de Recerca en Intel·ligència Artificial
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Fecha difusión:
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Resumen:
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[EN] 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 ...[+]
[EN] 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 sentencelevel 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.
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Palabras clave:
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Simultaneous machine translation
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Simultaneous MT
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Streaming MT
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Automatic speech recognition
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ASR
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PBE
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Derechos de uso:
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Reconocimiento (by)
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ISBN:
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978-1-955917-21-6
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Fuente:
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Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
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DOI:
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10.18653/v1/2022.acl-long.480
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Editorial:
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Association for Computational Linguistics
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Versión del editor:
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https://aclanthology.org/2022.acl-long.480
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Título del congreso:
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60th Annual Meeting of the Association for Computational Linguistics (ACL 2022)
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Lugar del congreso:
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Dublin, Ireland
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Fecha congreso:
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Mayo 22-27,2022
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Código del Proyecto:
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094879-B-I00/ES/SUBTITULACION MULTILINGUE DE CLASES DE AULA Y SESIONES PLENARIAS/
...[+]
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094879-B-I00/ES/SUBTITULACION MULTILINGUE DE CLASES DE AULA Y SESIONES PLENARIAS/
info:eu-repo/grantAgreement/EC/H2020/761758/EU/X5gon: Cross Modal, Cross Cultural, Cross Lingual, Cross Domain, and Cross Site Global OER Network/X5gon
info:eu-repo/grantAgreement/EC/H2020/952215/EU/Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization/TAILOR
info:eu-repo/grantAgreement/GVA//PROMETEO%2F2019%2F111/ES/CLASSROOM ACTIVITY RECOGNITION/
info:eu-repo/grantAgreement/EC/Erasmus+/2020-1-SI01-KA226-SCH-093604/ES/Educational eXplanations and Practices in Emergency Remote Teaching/
info:eu-repo/grantAgreement/MIU//FPU18%2F04135/ES/NOVEL CONTRIBUTIONS TO NEURAL SPEECH TRANSLATION/
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Agradecimientos:
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The research leading to these results has received funding from the European Union¿s Horizon 2020 research and innovation programme under grant agreements no. 761758 (X5Gon) and 952215 (TAILOR), and Erasmus+ Education ...[+]
The research leading to these results has received funding from the European Union¿s Horizon 2020 research and innovation programme under grant agreements no. 761758 (X5Gon) and 952215 (TAILOR), and Erasmus+ Education programme under grant agreement no. 20-226-093604-SCH (EXPERT); the Government of Spain¿s grant RTI2018-094879-B-I00 (Multisub) funded by MCIN/AEI/10.13039/501100011033 &
¿ERDF A way of making Europe¿, and FPU scholarships FPU18/04135; and the Generalitat Valenciana¿s research project Classroom Activity Recognition (ref. PROMETEO/2019/111). The authors gratefully acknowledge the computer resources at Artemisa, funded by the European Union ERDF and Comunitat Valenciana as well as the technical support provided by the Instituto de Física Corpuscular, IFIC (CSIC-UV).
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Tipo:
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Comunicación en congreso
Capítulo de libro
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