Machine Translation of Restaurant Reviews: New Corpus for Domain Adaptation and Robustness
This work addresses the need for better machine translation in real-world scenarios like restaurant reviews, but it is incremental as it builds on existing robustness techniques.
The authors tackled the problem of improving neural machine translation for user-generated restaurant reviews by releasing a new French-English parallel corpus and establishing a domain adaptation task, achieving significant improvements over existing online systems in both automatic and human evaluations.
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.