Nematus: a Toolkit for Neural Machine Translation

Abstract

We present Nematus, a toolkit for Neural Machine Translation. The toolkit prioritizes high translation accuracy, usability, and extensibility. Nematus has been used to build top-performing submissions to shared translation tasks at WMT and IWSLT, and has been used to train systems for production environments.

EACL 2017 demo track

References in corpus (2)

• Adam: A Method for Stochastic Optimization
• ADADELTA: An Adaptive Learning Rate Method

Cited by in corpus (9)

• RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition
• Exploiting Cross-Sentence Context for Neural Machine Translation
• Doubly Attentive Transformer Machine Translation
• Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation
• ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Ranking
• English-Bhojpuri SMT System: Insights from the Karaka Model
• Learning to Discriminate Noises for Incorporating External Information in Neural Machine Translation
• Multilingual Neural Machine Translation for Zero-Resource Languages
• Logographic Subword Model for Neural Machine Translation