Seq2seq for NLP: encoder-decoder framework for Tensorflow

General Purpose: We initially built this framework for Machine Translation, but have since used it for a variety of other tasks, including Summarization, Conversational Modeling, and Image Captioning. As long as your problem can be phrased as encoding input data in one format and decoding it into another format, you should be able to use or extend this framework. Usability: You can train a model with a single command. Several types of input data are supported, including standard raw text. Reproducibility: Training pipelines and models are configured using YAML files.