Joint ASR and MT Features for Quality Estimation in Spoken Language Translation

Abstract : This paper aims to unravel the automatic quality assessment for spoken language translation (SLT). More precisely, we propose several effective estimators based on our estimation of transcription (ASR) quality, translation (MT) quality, or both (combined and joint features using ASR and MT information). Our experiments provide an important opportunity to advance the understanding of the prediction quality of words in a SLT output that were revealed by MT and ASR features. These results could be applied to interactive speech translation or computer-assisted translation of speeches and lectures. For reproducible experiments, the code allowing to call our WCE-LIG application and the corpora used are made available to the research community.
Document type :
Conference papers
Liste complète des métadonnées

Cited literature [26 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01408087
Contributor : Laurent Besacier <>
Submitted on : Saturday, December 3, 2016 - 8:16:08 AM
Last modification on : Thursday, April 4, 2019 - 10:18:05 AM
Document(s) archivé(s) le : Monday, March 20, 2017 - 10:41:15 PM

File

iwslt2016-1.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01408087, version 1

Collections

Citation

Ngoc-Tien Le, Benjamin Lecouteux, Laurent Besacier. Joint ASR and MT Features for Quality Estimation in Spoken Language Translation. International Workshop on Spoken Language Translation, Dec 2016, Seattle, United States. ⟨hal-01408087⟩

Share

Metrics

Record views

241

Files downloads

184