As Little as Possible, as Much as Necessary: Detecting Over- and Undertranslations with Contrastive Conditioning

• URL: http://arxiv.org/abs/2203.01927v1
• Date: Thu, 3 Mar 2022 18:59:02 GMT
• Title: As Little as Possible, as Much as Necessary: Detecting Over- and Undertranslations with Contrastive Conditioning
• Authors: Jannis Vamvas and Rico Sennrich
• Abstract summary: We propose a method for detecting superfluous words in neural machine translation. We compare the likelihood of a full sequence under a translation model to the likelihood of its parts, given the corresponding source or target sequence. This allows to pinpoint superfluous words in the translation and untranslated words in the source even in the absence of a reference translation.
• Score: 42.46681912294797
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Omission and addition of content is a typical issue in neural machine translation. We propose a method for detecting such phenomena with off-the-shelf translation models. Using contrastive conditioning, we compare the likelihood of a full sequence under a translation model to the likelihood of its parts, given the corresponding source or target sequence. This allows to pinpoint superfluous words in the translation and untranslated words in the source even in the absence of a reference translation. The accuracy of our method is comparable to a supervised method that requires a custom quality estimation model.

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