Multi-Domain Adaptation in Neural Machine Translation Through Multidimensional Tagging

Many modern Neural Machine Translation (NMT) systems are trained on nonhomogeneous datasets with several distinct dimensions of variation (e.g. domain, source, generation method, style, etc.). We describe and empirically evaluate multidimensional tagging (MDT), a simple yet effective method for passing sentence-level information to the model. Our human and BLEU evaluation results show that MDT can be applied to the problem of multi-domain adaptation and significantly reduce training costs without sacrificing the translation quality on any of the constituent domains.

Efficient Image Gallery Representations at Scale Through Multi-Task Learning

Image galleries provide a rich source of diverse information about a product which can be leveraged across many recommendation and retrieval applications. We study the problem of building a universal image gallery encoder through multi-task learning (MTL) approach and demonstrate that it is indeed a practical way to achieve generalizability of learned representations to new downstream tasks. Additionally, we analyze the relative predictive performance of MTL-trained solutions against optimal and substantially more expensive solutions, and find signals that MTL can be a useful mechanism to address sparsity in low-resource binary tasks.

Toward a full-scale neural machine translation in production: the Booking.com use case

While some remarkable progress has been made in neural machine translation (NMT) research, there have not been many reports on its development and evaluation in practice. This paper tries to fill this gap by presenting some of our findings from building an in-house travel domain NMT system in a large scale E-commerce setting. The three major topics that we cover are optimization and training (including different optimization strategies and corpus sizes), handling real-world content and evaluating results.

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.