Mach Translat https://doi.org/10.1007/s10590-018-9218-6 Automatic quality estimation for speech translation using joint ASR and MT features 1 1 Ngoc-Tien Le · Benjamin Lecouteux · Laurent Besacier Received: 29 July 2016 / Accepted: 22 March 2018 © Springer Science+Business Media B.V., part of Springer Nature 2018 Abstract This paper addresses the automatic quality estimation of spoken language translation (SLT). This relatively new task is deﬁned and formalized as a sequence- labeling problem where each word in the SLT hypothesis is tagged as good or bad according to a large feature set. We propose several word conﬁdence estimators (WCE) based on our automatic evaluation of transcription (ASR) quality, translation (MT) quality, or both (combined ASR + MT). This research work is possible because we built a speciﬁc corpus, which contains 6.7k utterances comprising the quintuplet: ASR output, verbatim transcript, text translation, speech translation, and post-edition of the translation. The conclusion of our multiple experiments using joint ASR and MT features for WCE is that MT features remain the most inﬂuential while ASR features can bring interesting complementary information. In addition, the last part of the paper proposes to disentangle ASR errors and MT errors where each word in the SLT hypothesis is
Machine Translation – Springer Journals
Published: Jun 1, 2018
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