# Machine Translation of Restaurant Reviews: New Corpus for Domain Adaptation and Robustness

arXiv: https://arxiv.org/abs/1910.14589 · HTML: https://mlsanity.com/paper/1910.14589/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2019.

## Built on by (most cited first, 10 of 10)

- [Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspection](https://mlsanity.com/paper/2301.07779/) (2023)
- [On Compositional Generalization of Neural Machine Translation](https://mlsanity.com/paper/2105.14802/) (2021)
- [Multilingual Domain Adaptation for NMT: Decoupling Language and Domain Information with Adapters](https://mlsanity.com/paper/2110.09574/) (2021)
- [Understanding the Impact of UGC Specificities on Translation Quality](https://mlsanity.com/paper/2110.12551/) (2021)
- [Speeding Up Entmax](https://mlsanity.com/paper/2111.06832/) (2021)
- [Faithful Target Attribute Prediction in Neural Machine Translation](https://mlsanity.com/paper/2109.12105/) (2021)
- [Investigating the potential of Sparse Mixtures-of-Experts for multi-domain neural machine translation](https://mlsanity.com/paper/2407.01126/) (2024)
- [Machine Translation Robustness to Natural Asemantic Variation](https://mlsanity.com/paper/2205.12514/) (2022)
- [An Empirical Study on the Robustness of Massively Multilingual Neural Machine Translation](https://mlsanity.com/paper/2405.07673/) (2024)
- [When the Gold Standard Isn't Necessarily Standard: Challenges of Evaluating the Translation of User-Generated Content](https://mlsanity.com/paper/2512.17738/) (2025)

## 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.