Machine Translation at Booking.com: Journey and Lessons Learned
We describe our recently developed neural machine translation (NMT) system and benchmark it against our own statistical machine translation (SMT) system as well as two other general purpose online engines (statistical and neural). We present automatic and human evaluation results of the translation output provided by each system. We also analyze the effect of sentence length on the quality of output for SMT and NMT systems.
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationNMTSentenceTranslationSimilar Papers 제목 키워드 기반
Machine translation at Booking.com: what's next?
Managing Diversity in Airbnb Search
One of the long-standing questions in search systems is the role of diversity in results. From a product perspective, showing diverse results provides the user with more choice and should lead to an improved experience. …
DiversityThe MITLL-AFRL IWSLT 2016 Systems
This report summarizes the MITLL-AFRL MT and ASR systems and the experiments run during the 2016 IWSLT evaluation campaign. Building on lessons learned from previous years’ results, we refine our ASR systems and examine …
Machine TranslationTranslationMaturity Framework for Enhancing Machine Learning Quality
With the rapid integration of Machine Learning (ML) in business applications and processes, it is crucial to ensure the quality, reliability and reproducibility of such systems. We suggest a methodical approach towards M…