Improving Robustness in Real-World Neural Machine Translation Engines

TL;DR

This paper discusses challenges in deploying neural machine translation systems in real-world settings and presents strategies to enhance their robustness across diverse variables and use cases.

Contribution

The paper introduces specific issues faced in practical NMT deployment and proposes approaches to improve model robustness in real-world scenarios.

Findings

Identified key robustness challenges in real-world NMT

Proposed methods to mitigate variability impacts

Enhanced NMT robustness demonstrated in practical applications

Abstract

As a commercial provider of machine translation, we are constantly training engines for a variety of uses, languages, and content types. In each case, there can be many variables, such as the amount of training data available, and the quality requirements of the end user. These variables can have an impact on the robustness of Neural MT engines. On the whole, Neural MT cures many ills of other MT paradigms, but at the same time, it has introduced a new set of challenges to address. In this paper, we describe some of the specific issues with practical NMT and the approaches we take to improve model robustness in real-world scenarios.