Prague Bulletin of Mathematical Linguistics 107 Pages 1-11 Charles University 2017.
In this paper, we announce development of Neural Monkey --- an open-source neural machine translation (NMT) and general sequence-to-sequence learning system built over TensorFlow machine learning library.
The system provides a high-level API with support for fast prototyping of complex architectures with multiple sequence encoders and decoders.
These models’ overall architecture is specified in easy-to-read configuration files.
The long-term goal of Neural Monkey project is to create and maintain a growing collection of implementations of recently proposed components or methods, and therefore it is designed to be easily extensible.
The trained models can be deployed either for batch data processing or as a web service.
In the presented paper, we describe the design of the system and introduce the reader to running experiments using Neural Monkey.