THUMT: An Open Source Toolkit for Neural Machine Translation
Machine translation is a natural language processing task that aims to translate natural languages using computers automatically. Recent several years have witnessed the rapid development of end-to-end neural machine translation, which has become the new mainstream method in practical MT systems.
THUMT is an open-source toolkit for neural machine translation developed by the Natural Language Processing Group at Tsinghua University.
THUMT has currently two main implementations:
THUMT-TensorFlow: a new implementation developed with TensorFlow. It implements the sequence-to-sequence model (Seq2Seq) (Sutskever et al., 2014), the standard attention-based model (RNNsearch) (Bahdanau et al., 2014), and the Transformer model (Transformer) (Vaswani et al., 2017).
THUMT-Theano: the original project developed with Theano, which is no longer updated because MLA put an end to Theano. It implements the standard attention-based model (RNNsearch) (Bahdanau et al., 2014), minimum risk training (MRT) (Shen et al., 2016) for optimizing model parameters with respect to evaluation metrics, semi-supervised training (SST) (Cheng et al., 2016) for exploiting monolingual corpora to learn bi-directional translation models, and layer-wise relevance propagation (LRP) (Ding et al., 2017) for visualizing and anlayzing RNNsearch.
The following table summarizes the features of two implementations:
|Theano||RNNsearch||MLE, MRT, SST||SGD, AdaDelta, Adam||RNNsearch|
|TensorFlow||Seq2Seq, RNNsearch, Transformer||MLE||Adam||n/a|
Please cite the following paper:
Jiacheng Zhang, Yanzhuo Ding, Shiqi Shen, Yong Cheng, Maosong Sun, Huanbo Luan, Yang Liu. 2017. THUMT: An Open Source Toolkit for Neural Machine Translation. arXiv:1706.06415.
Project members: Jiacheng Zhang, Yanzhuo Ding, Shiqi Shen, Yong Cheng
- Zhixing Tan (Xiamen University)
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