THUMT: An Open Source Toolkit for Neural Machine Translation

Abstract

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.

4 pages, 1 figure

References in corpus (3)

• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• ADADELTA: An Adaptive Learning Rate Method
• Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions

Cited by in corpus (6)

• Convolutional Self-Attention Networks
• Shared-Private Bilingual Word Embeddings for Neural Machine Translation
• MultiMWE: Building a Multi-lingual Multi-Word Expression (MWE) Parallel Corpora
• Neuron Interaction Based Representation Composition for Neural Machine Translation
• Learning to Copy for Automatic Post-Editing
• Learning to Detect Unacceptable Machine Translations for Downstream Tasks