Efficient and Flexible Implementation of Machine Learning for ASR and MT

Abstract: Flexibility and speed are key features for a deep learning framework to allow fast transition from a research idea to prototyping and production code. We outline how to implement a unified framework for sequence processing that covers various kinds of models and applications. We will discuss our toolkit RETURNN as an example for such an implementation, that is easy to apply and understand for the user, flexible to allow for any kind of architecture or method, and at the same time also very efficient. In addition, a comparison of the properties of different machine learning toolkits for sequence classification is provided. The flexibility of using such specific implementations will be demonstrated describing the setup of recent state-of-the-art models for automatic speech recognition and machine translation, upon others.