Zero

A neural machine translation system implemented by python2 + tensorflow.

Features

1. Multi-Process Data Loading/Processing (Problems Exist)
2. Multi-GPU Training/Decoding
3. Gradient Aggregation

Papers

We associate each paper below with a readme file link. Please click the paper link you are interested for more details.

• Efficient CTC Regularization via Coarse Labels for End-to-End Speech Translation, EACL2023
• Revisiting End-to-End Speech-to-Text Translation From Scratch, ICML2022
• Sparse Attention with Linear Units, EMNLP2021
• Edinburgh's End-to-End Multilingual Speech Translation System for IWSLT 2021, IWSLT2021 System submission
• Beyond Sentence-Level End-to-End Speech Translation: Context Helps, ACL2021
• On Sparsifying Encoder Outputs in Sequence-to-Sequence Models, ACL2021 Findings
• Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation, ICLR2021
• Fast Interleaved Bidirectional Sequence Generation, WMT2020
• Adaptive Feature Selection for End-to-End Speech Translation, EMNLP2020 Findings
• Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation, ACL2020
• Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention, EMNLP2019

Supported Models

• RNNSearch: support LSTM, GRU, SRU, ATR, EMNLP2018, and LRN, ACL2019 models.
• Deep attention: Neural Machine Translation with Deep Attention, TPAMI
• CAEncoder: the context-aware recurrent encoder, see the paper, TASLP and the original source code (in Theano).
• Transformer: attention is all you need
• AAN: the average attention model, ACL2018 that accelerates the decoding!
• Fixup: Fixup Initialization: Residual Learning Without Normalization
• Relative position representation: Self-Attention with Relative Position Representations

Requirements

• python2.7
• tensorflow <= 1.13.2

Usage

How to use this toolkit for machine translation?

TODO:

1. organize the parameters and interpretations in config.
2. reformat and fulfill code comments
3. simplify and remove unecessary coding
4. improve rnn models

Citation

If you use the source code, please consider citing the follow paper:

@InProceedings{D18-1459,
  author = 	"Zhang, Biao
		and Xiong, Deyi
		and su, jinsong
		and Lin, Qian
		and Zhang, Huiji",
  title = 	"Simplifying Neural Machine Translation with Addition-Subtraction Twin-Gated Recurrent Networks",
  booktitle = 	"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
  year = 	"2018",
  publisher = 	"Association for Computational Linguistics",
  pages = 	"4273--4283",
  location = 	"Brussels, Belgium",
  url = 	"http://aclweb.org/anthology/D18-1459"
}

If you are interested in the CAEncoder model, please consider citing our TASLP paper:

@article{Zhang:2017:CRE:3180104.3180106,
 author = {Zhang, Biao and Xiong, Deyi and Su, Jinsong and Duan, Hong},
 title = {A Context-Aware Recurrent Encoder for Neural Machine Translation},
 journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
 issue_date = {December 2017},
 volume = {25},
 number = {12},
 month = dec,
 year = {2017},
 issn = {2329-9290},
 pages = {2424--2432},
 numpages = {9},
 url = {https://doi.org/10.1109/TASLP.2017.2751420},
 doi = {10.1109/TASLP.2017.2751420},
 acmid = {3180106},
 publisher = {IEEE Press},
 address = {Piscataway, NJ, USA},
}

Reference

When developing this repository, I referred to the following projects:

• Nematus
• THUMT
• Tensor2Tensor
• Keras

Contact

For any questions or suggestions, please feel free to contact Biao Zhang