2 research outputs found
THUMT: An Open Source Toolkit for Neural Machine Translation
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.Comment: 4 pages, 1 figur
Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization
Although neural machine translation has made significant progress recently,
how to integrate multiple overlapping, arbitrary prior knowledge sources
remains a challenge. In this work, we propose to use posterior regularization
to provide a general framework for integrating prior knowledge into neural
machine translation. We represent prior knowledge sources as features in a
log-linear model, which guides the learning process of the neural translation
model. Experiments on Chinese-English translation show that our approach leads
to significant improvements.Comment: ACL 2017 (modified