Machine Translation of Restaurant Reviews: New Corpus for Domain Adaptation and Robustness
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Abstract (arXiv)
We share a French-English parallel corpus of Foursquare restaurant reviews (https://europe.naverlabs.com/research/natural-language-processing/machine-translation-of-restaurant-reviews), and define a new task to encourage research on Neural Machine Translation robustness and domain adaptation, in a real-world scenario where better-quality MT would be greatly beneficial. We discuss the challenges of such user-generated content, and train good baseline models that build upon the latest techniques for MT robustness. We also perform an extensive evaluation (automatic and human) that shows significant improvements over existing online systems. Finally, we propose task-specific metrics based on sentiment analysis or translation accuracy of domain-specific polysemous words.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Oct 2019.
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Citing papers per year: 2021: 5; 2022: 1; 2023: 1; 2024: 2; 2025: 1.
10 papers.
- 2023Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspectioncited by 44
- 2021On Compositional Generalization of Neural Machine Translationcited by 34
- 2021Multilingual Domain Adaptation for NMT: Decoupling Language and Domain Information with Adapterscited by 21
- 2021Understanding the Impact of UGC Specificities on Translation Qualitycited by 3
- 2021Speeding Up Entmaxcited by 3
- 2021Faithful Target Attribute Prediction in Neural Machine Translationcited by 2
- 2024Investigating the potential of Sparse Mixtures-of-Experts for multi-domain neural machine translationcited by 1
- 2022Machine Translation Robustness to Natural Asemantic Variationcited by 0
- 2024An Empirical Study on the Robustness of Massively Multilingual Neural Machine Translationcited by 0
- 2025When the Gold Standard Isn't Necessarily Standard: Challenges of Evaluating the Translation of User-Generated Contentcited by 0
- 2025When the Gold Standard Isn't Necessarily Standard: Challenges of Evaluating the Translation of User-Generated Content19 Dec 2025
- 2024Investigating the potential of Sparse Mixtures-of-Experts for multi-domain neural machine translation1 Jul 2024
- 2024An Empirical Study on the Robustness of Massively Multilingual Neural Machine Translation13 May 2024
- 2023Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspection18 Jan 2023
- 2022Machine Translation Robustness to Natural Asemantic Variation25 May 2022
- 2021Speeding Up Entmax12 Nov 2021
- 2021Understanding the Impact of UGC Specificities on Translation Quality24 Oct 2021
- 2021Multilingual Domain Adaptation for NMT: Decoupling Language and Domain Information with Adapters18 Oct 2021
- 2021Faithful Target Attribute Prediction in Neural Machine Translation24 Sep 2021
- 2021On Compositional Generalization of Neural Machine Translation31 May 2021