A neural machine translation system implemented by python2 + tensorflow.
- Multi-Process Data Loading/Processing (Problems Exist)
- Multi-GPU Training/Decoding
- Gradient Aggregation
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
- 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
- python2.7
- tensorflow <= 1.13.2
How to use this toolkit for machine translation?
- organize the parameters and interpretations in config.
- reformat and fulfill code comments
- simplify and remove unecessary coding
- improve rnn models
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},
}
When developing this repository, I referred to the following projects:
For any questions or suggestions, please feel free to contact Biao Zhang