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

Vamvas, Jannis; Sennrich, Rico (2022). As Little as Possible, as Much as Necessary: Detecting Over- and Undertranslations with Contrastive Conditioning. In: 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland, May 2022. Association for Computational Linguistics, 490-500.

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.

Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Computational Linguistics
Dewey Decimal Classification:000 Computer science, knowledge & systems
410 Linguistics
Language:English
Event End Date:May 2022
Deposited On:19 May 2022 08:01
Last Modified:25 May 2022 11:27
Publisher:Association for Computational Linguistics
Additional Information:Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics Volume 2: Short Papers, pages 490 - 500 May 22-27, 2022 c©2022 Association for Computational Linguistics
OA Status:Green
Free access at:Official URL. An embargo period may apply.
Official URL:https://aclanthology.org/2022.acl-short.53/
Related URLs:https://github.com/zurichnlp/coverage-contrastive-conditioning (Research Data)
Project Information:
  • Funder: SNSF
  • Grant ID: PP00P1_176727
  • Project Title: Multi-Task Learning with Multilingual Resources for Better Natural Language Understanding
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  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)

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