Abstract / Description of output
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.
Original language | English |
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Title of host publication | Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) |
Editors | Smaranda Muresan, Preslav Nakov, Aline Vlliavicencio |
Publisher | Association for Computational Linguistics |
Pages | 490-500 |
Number of pages | 11 |
Volume | 2 |
ISBN (Print) | 978-1-955917-22-3 |
Publication status | Published - 16 May 2022 |
Event | 60th Annual Meeting of the Association for Computational Linguistics - The Convention Centre Dublin, Dublin, Ireland Duration: 22 May 2022 → 27 May 2022 https://www.2022.aclweb.org |
Conference
Conference | 60th Annual Meeting of the Association for Computational Linguistics |
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Abbreviated title | ACL 2022 |
Country/Territory | Ireland |
City | Dublin |
Period | 22/05/22 → 27/05/22 |
Internet address |