diff --git a/README.md b/README.md
index 92b326a..7f23bea 100644
--- a/README.md
+++ b/README.md
@@ -5,19 +5,19 @@ This repository builds on the idea of back translation [1] as a data augmentatio
-In this project we provide a nice interface for people to investigate back-translation models interactively that works with any `tensor2tensor` checkpoints. We also provide the option to perform back-translation in batch mode for back-translating a full dataset, see [this section](https://github.com/vietai/back_translate#notebook-a-case-study-on-back-translation-for-low-resource-languages). Here we provide two sets of trained checkpoints:
+In this project we provide a nice interface for people to investigate back-translation models interactively that works with any `tensor2tensor` checkpoints. We also provide the option to perform back-translation in batch mode for back-translating a full dataset, see [this section](https://github.com/vietai/dab#notebook-a-case-study-on-back-translation-for-low-resource-languages). Here we provide two sets of trained checkpoints:
-* English - Vietnamese: [[en-vi]](https://console.cloud.google.com/storage/browser/vien-translation/checkpoints/translate_envi_iwslt32k_tiny/avg/) [[vi-en]](https://console.cloud.google.com/storage/browser/vien-translation/checkpoints/translate_vien_iwslt32k_tiny/avg/). See [Appendix A](https://github.com/vietai/back_translate#appendix-a-training-translation-models-with-tensor2tensor) for how to train and visualize your own translation models.
+* English - Vietnamese: [[en-vi]](https://console.cloud.google.com/storage/browser/vien-translation/checkpoints/translate_envi_iwslt32k_tiny/avg/) [[vi-en]](https://console.cloud.google.com/storage/browser/vien-translation/checkpoints/translate_vien_iwslt32k_tiny/avg/). See [Appendix A](https://github.com/vietai/dab#appendix-a-training-translation-models-with-tensor2tensor) for how to train and visualize your own translation models.
* English - French: [[en-fr]](https://console.cloud.google.com/storage/browser/vien-translation/checkpoints/translate_enfr_fren_uda/enfr/) [[fr-en]](https://console.cloud.google.com/storage/browser/vien-translation/checkpoints/translate_enfr_fren_uda/fren). This is taken from [github repository of UDA](https://github.com/google-research/uda).
## :notebook: Interactive Back-translation.
-We use [this Colab Notebook](https://colab.research.google.com/github/vietai/back_translate/blob/master/colab/Interactive_Back_Translation.ipynb) to generate the GIF you saw above.
+We use [this Colab Notebook](https://colab.research.google.com/github/vietai/dab/blob/master/colab/Interactive_Back_Translation.ipynb) to generate the GIF you saw above.
## :notebook: A Case Study on Back-translation for Low-resource Languages
-Unsupervised Data Augmentation [3] has demonstrated improvements for high-resource languages (English) with back-translation. In this work, we conduct a case study for Vietnamese through the following [Colab Notebook](https://colab.research.google.com/github/vietai/back_translate/blob/master/colab/Sentiment_Analysis_%2B_Back_translation.ipynb).
+Unsupervised Data Augmentation [3] has demonstrated improvements for high-resource languages (English) with back-translation. In this work, we conduct a case study for Vietnamese through the following [Colab Notebook](https://colab.research.google.com/github/vietai/dab/blob/master/colab/Sentiment_Analysis_%2B_Back_translation.ipynb).
On a Sentiment Analysis dataset with only 10K examples, we use back-translation to double the training set size and obtain an improvement of near 2.5\% in absolute accuracy:
@@ -44,7 +44,7 @@ Here is another GIF demo with Vietnamese sentences - for fun ;)
## How to contribute? :thinking:
-:seedling: More and/or better translation models. Check out [Appendix A](https://github.com/vietai/back_translate#appendix-a-training-translation-models-with-tensor2tensor) for Colab Notebook tutorials on how to train translation models with `tensor2tensor`.
+:seedling: More and/or better translation models. Check out [Appendix A](https://github.com/vietai/dab#appendix-a-training-translation-models-with-tensor2tensor) for Colab Notebook tutorials on how to train translation models with `tensor2tensor`.
:seedling: More and/or better translation data or monolingual data.
@@ -60,7 +60,7 @@ We will be working on a more detailed guideline for contribution.
@article{trieu19backtranslate,
author = {Trieu H. Trinh and Thang Le and Phat Hoang and Minh{-}Thang Luong},
title = {Back Translation as Data Augmentation Tutorial},
- journal = {https://github.com/vietai/back_translate},
+ journal = {https://github.com/vietai/dab},
year = {2019},
}
```
@@ -77,7 +77,7 @@ We will be working on a more detailed guideline for contribution.
## Appendix A: Training Translation Models with `tensor2tensor`
-:notebook: [Training Translation Models](https://colab.research.google.com/github/vietai/back_translate/blob/master/colab/T2T_translate_vi%3C_%3Een_tiny_tpu.ipynb): How to connect to GPU/TPU and Google Drive/Cloud storage, download training/testing data from the internet and train/evaluate your models. We use the IWSLT'15 dataset for the English-Vietnamese pair, off-the-shelf Transformer implementation from `tensor2tensor` with its `transformer_tiny` setting and obtain the following result:
+:notebook: [Training Translation Models](https://colab.research.google.com/github/vietai/dab/blob/master/colab/T2T_translate_vi%3C_%3Een_tiny_tpu.ipynb): How to connect to GPU/TPU and Google Drive/Cloud storage, download training/testing data from the internet and train/evaluate your models. We use the IWSLT'15 dataset for the English-Vietnamese pair, off-the-shelf Transformer implementation from `tensor2tensor` with its `transformer_tiny` setting and obtain the following result:
@@ -102,7 +102,7 @@ We will be working on a more detailed guideline for contribution.
As of this writing, the result above is already competitive with the current state-of-the-art (29.6 BLEU score) [4], without using semi-supervised or multi-task learning. More importantly, this result is good enough to be useful for the purpose of this project! For English-French, we use the `transformer_big` provided in the [open-source implementation](https://github.com/google-research/uda) of Unsupervised Data Augmentation [3].
-:notebook: [Analyse your Translation Models](https://colab.research.google.com/github/vietai/back_translate/blob/master/colab/Vietnamese_Backtranslation_Model_Analysis.ipynb): Play with and visualize the trained models attention.
+:notebook: [Analyse your Translation Models](https://colab.research.google.com/github/vietai/dab/blob/master/colab/Vietnamese_Backtranslation_Model_Analysis.ipynb): Play with and visualize the trained models attention.
@@ -117,7 +117,7 @@ We make use of the `tensor2tensor` library to build deep neural networks that pe
### Training the two translation models
-A prerequisite to performing back-translation is to train two translation models: English to Vietnamese and Vietnamese to English. A demonstration of the following commands to generate data, train and evaluate the models can be found in [this Google Colab](https://colab.research.google.com/github/vietai/back_translate/blob/master/colab/T2T_translate_vi%3C_%3Een_tiny_tpu.ipynb).
+A prerequisite to performing back-translation is to train two translation models: English to Vietnamese and Vietnamese to English. A demonstration of the following commands to generate data, train and evaluate the models can be found in [this Google Colab](https://colab.research.google.com/github/vietai/dab/blob/master/colab/T2T_translate_vi%3C_%3Een_tiny_tpu.ipynb).
#### Generate data (tfrecords)
@@ -151,7 +151,7 @@ python t2t_trainer.py --data_dir=path/to/tfrecords --problem=translate_vien_iwsl
#### Analyse the trained models
-Once you finished training and evaluating the models, you can certainly play around with them a bit. For example, you might want to run some interactive translation and/or visualize the attention masks for your inputs of choice. This is demonstrated in [this Google Colab](https://colab.research.google.com/github/vietai/back_translate/blob/master/colab/Vietnamese_Backtranslation_Model_Analysis.ipynb).
+Once you finished training and evaluating the models, you can certainly play around with them a bit. For example, you might want to run some interactive translation and/or visualize the attention masks for your inputs of choice. This is demonstrated in [this Google Colab](https://colab.research.google.com/github/vietai/dab/blob/master/colab/Vietnamese_Backtranslation_Model_Analysis.ipynb).
### Back translate from a text file.
@@ -163,6 +163,6 @@ Here is an example of back translating Vietnamese -> English -> Vietnamese from
python back_translate.py --lang=vi --decode_hparams="beam_size=4,alpha=0.6" --paraphrase_from_file=test_input.vi --paraphrase_to_file=test_output.vi --model=transformer --hparams_set=transformer_tiny
```
-Add `--backtraslate_interactively` to back-translate interactively from your terminal. Alternatively, you can also check out [this Colab](https://colab.research.google.com/github/vietai/back_translate/blob/master/colabs/Interactive_Back_Translation.ipynb).
+Add `--backtraslate_interactively` to back-translate interactively from your terminal. Alternatively, you can also check out [this Colab](https://colab.research.google.com/github/vietai/dab/blob/master/colabs/Interactive_Back_Translation.ipynb).
-For a demonstration of augmenting real datasets with back-translation and obtaining actual gains in accuracy, check out [this Google Colab](https://colab.research.google.com/github/vietai/back_translate/blob/master/colab/Sentiment_Analysis_%2B_Back_translation.ipynb)!
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+For a demonstration of augmenting real datasets with back-translation and obtaining actual gains in accuracy, check out [this Google Colab](https://colab.research.google.com/github/vietai/dab/blob/master/colab/Sentiment_Analysis_%2B_Back_translation.ipynb)!
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