  This paper introduces THUMT, an open-source toolkit for neural machine
translation (NMT) developed by the Natural Language Processing Group at
Tsinghua University. THUMT implements the standard attention-based
encoder-decoder framework on top of Theano and supports three training
criteria: maximum likelihood estimation, minimum risk training, and
semi-supervised training. It features a visualization tool for displaying the
relevance between hidden states in neural networks and contextual words, which
helps to analyze the internal workings of NMT. Experiments on Chinese-English
datasets show that THUMT using minimum risk training significantly outperforms
GroundHog, a state-of-the-art toolkit for NMT.
