<commit_before>---
layout: page
title: About
order: 5
---


OpenNMT was originally developed by <a href="http://yoon.io"><NAME></a> and <a href="http://nlp.seas.harvard.edu">harvardnlp</a>.

<center>
<img style="margin:40px" width="200px" src="http://lstm.seas.harvard.edu/logo_nlp.png" />
</center>

Major source contributions and support come from <a href="http://www.systransoft.com/">SYSTRAN</a>.

<center>
<img style="margin:40px" width="200px" src="http://www.systransoft.com/wp-content/themes/systran/img/common/logo.png" />
</center>

## Research

The main model is based on the papers [Neural Machine Translation by Jointly Learning to Align and Translate](https://arxiv.org/abs/1409.0473) Bahdanau et al. ICLR 2015 and
[Effective Approaches to Attention-based
Neural Machine Translation](http://stanford.edu/~lmthang/data/papers/emnlp15_attn.pdf),
Luong et al. EMNLP 2015.


There are a lot of additional options on top of the baseline model, mainly thanks to the fantastic folks
at [SYSTRAN](http://www.systransoft.com). Specifically, there are functionalities which implement:

* [Effective Approaches to Attention-based Neural Machine Translation](http://stanford.edu/~lmthang/data/papers/emnlp15_attn.pdf). Luong et al., EMNLP 2015.
* [Character-based Neural Machine Translation](https://aclweb.org/anthology/P/P16/P16-2058.pdf). Costa-Jussa and Fonollosa, ACL 2016.
* [Compression of Neural Machine Translation Models via Pruning](https://arxiv.org/pdf/1606.09274.pdf). See et al., CoNLL 2016.
* [Sequence-Level Knowledge Distillation](https://arxiv.org/pdf/1606.07947.pdf). Kim and Rush., EMNLP 2016.
* [Deep Recurrent Models with Fast Forward Connections for Neural Machine Translation](https://arxiv.org/pdf/1606.04199).
Zhou et al, TACL 2016.
* [Guided Alignment Training for Topic-Aware Neural Machine Translation](https://arxiv.org/pdf/1607.01628). Chen et al., arXiv:1607.01628.
* [Linguistic Input Features Improve Neural Machine Translation](https://arxiv.org/pdf/1606.02892). Senrich et al., arXiv:1606.02892
<commit_msg>add link to BPE<commit_after>---
layout: page
title: About
order: 5
---


OpenNMT was originally developed by <a href="http://yoon.io"><NAME></a> and <a href="http://nlp.seas.harvard.edu">harvardnlp</a>.

<center>
<img style="margin:40px" width="200px" src="http://lstm.seas.harvard.edu/logo_nlp.png" />
</center>

Major source contributions and support come from <a href="http://www.systransoft.com/">SYSTRAN</a>.

<center>
<img style="margin:40px" width="200px" src="http://www.systransoft.com/wp-content/themes/systran/img/common/logo.png" />
</center>

## Research

The main model is based on the papers [Neural Machine Translation by Jointly Learning to Align and Translate](https://arxiv.org/abs/1409.0473) Bahdanau et al. ICLR 2015 and
[Effective Approaches to Attention-based
Neural Machine Translation](http://stanford.edu/~lmthang/data/papers/emnlp15_attn.pdf),
Luong et al. EMNLP 2015.


There are a lot of additional options on top of the baseline model, mainly thanks to the fantastic folks
at [SYSTRAN](http://www.systransoft.com). Specifically, there are functionalities which implement:

* [Effective Approaches to Attention-based Neural Machine Translation](http://stanford.edu/~lmthang/data/papers/emnlp15_attn.pdf). Luong et al., EMNLP 2015.
* [Character-based Neural Machine Translation](https://aclweb.org/anthology/P/P16/P16-2058.pdf). Costa-Jussa and Fonollosa, ACL 2016.
* [Compression of Neural Machine Translation Models via Pruning](https://arxiv.org/pdf/1606.09274.pdf). See et al., CoNLL 2016.
* [Sequence-Level Knowledge Distillation](https://arxiv.org/pdf/1606.07947.pdf). Kim and Rush., EMNLP 2016.
* [Deep Recurrent Models with Fast Forward Connections for Neural Machine Translation](https://arxiv.org/pdf/1606.04199).
Zhou et al, TACL 2016.
* [Guided Alignment Training for Topic-Aware Neural Machine Translation](https://arxiv.org/pdf/1607.01628). Chen et al., arXiv:1607.01628.
* [Linguistic Input Features Improve Neural Machine Translation](https://arxiv.org/pdf/1606.02892). Senrich et al., arXiv:1606.02892
* [Neural Machine Translation of Rare Words with Subword Units](https://www.aclweb.org/anthology/P/P16/P16-1162.pdf). Senrich et al., ACL 2016.
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{'dir': 'git-commits-cleaned', 'id': '2778383', 'n_tokens_mistral': 1653, 'n_tokens_neox': 1373, 'n_words': 383}