Lexicons and Minimum Risk Training for Neural Machine Translation: NAIST-CMU at WAT2016

2016/10/20 by Graham Neubig, Neubig, Graham
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Multimodal Machine Learning Applications #Natural Language Processing Techniques #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1610.06542

openalex publication_date 2016/10/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This year, the Nara Institute of Science and Technology (NAIST)/Carnegie Mellon University (CMU) submission to the Japanese-English translation track of the 2016 Workshop on Asian Translation was based on attentional neural machine translation (NMT) models. In addition to the standard NMT model, we make a number of improvements, most notably the use of discrete translation lexicons to improve probability estimates, and the use of minimum risk training to optimize the MT system for BLEU score. As a result, our system achieved the highest translation evaluation scores for the task.

Citations

• Adam: A Method for Stochastic Optimization
• Long Short-Term Memory
• Effective Approaches to Attention-based Neural Machine Translation

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• Incorporating Discrete Translation Lexicons into Neural Machine Translation
• Neural Machine Translation Advised by Statistical Machine Translation
• Neural Machine Translation: Challenges, Progress and Future
• Neural Machine Translation: A Review of Methods, Resources, and Tools