This paper presents an in-depth investigation on integrating neural language
models in translation systems. Scaling neural language models is a difficult
task, but crucial for real-world applications. This paper evaluates the impact
on end-to-end MT quality of both new and existing scaling techniques. We show
when explicitly normalising neural models is necessary and what optimisation
tricks one should use in such scenarios. We also focus on scalable training
algorithms and investigate noise contrastive estimation and diagonal contexts
as sources for further speed improvements. We explore the trade-offs between
neural models and back-off n-gram models and find that neural models make
strong candidates for natural language applications in memory constrained
environments, yet still lag behind traditional models in raw translation
quality. We conclude with a set of recommendations one should follow to build a
scalable neural language model for MT.

该研究论文深入探讨了集成神经语言模型在翻译系统中的应用，评估了多种扩展神经模型的技术对最终翻译质量的影响，并提出了一系列训练算法、优化技巧和建议，以构建一个适用于机器翻译的可扩展的神经语言模型。