https://github.com/sheffieldnlp/deepQuest-py
Large and Distilled Models for Quality Estimation of Machine Translation
https://github.com/sheffieldnlp/deepQuest-py
deep-learning machine-translation natural-language-processing quality-estimation
Last synced: 8 months ago
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Large and Distilled Models for Quality Estimation of Machine Translation
- Host: GitHub
- URL: https://github.com/sheffieldnlp/deepQuest-py
- Owner: sheffieldnlp
- License: other
- Created: 2021-01-19T16:29:15.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2021-11-25T14:26:40.000Z (over 4 years ago)
- Last Synced: 2024-10-11T18:07:52.835Z (over 1 year ago)
- Topics: deep-learning, machine-translation, natural-language-processing, quality-estimation
- Language: Python
- Homepage:
- Size: 2.67 MB
- Stars: 7
- Watchers: 13
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.bib
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README
# deepQuest-py
deepQuest-py is a framework for training and evaluation of models for Quality Estimation of Machine Translation.
This is a new version of [deepQuest](https://github.com/sheffieldnlp/deepQuest) - the first framework for neural Quality Estimation.
deepQuest-py provides:
- **high performing** sentence-level and word-level models based on finetuning pre-trained Transformers;
- **light-weight and efficient** sentence-level models implemented via **knowledge distillation**.
deepQuest-py includes implementations of several approaches for Quality Estimation proposed in recent research:
- [Knowledge Distillation for Quality Estimation (Gajbhiye et al., 2021)](https://github.com/sheffieldnlp/deepQuest-py/tree/main/examples/knowledge_distillation)
- [TransQuest at WMT2020: Sentence-Level Direct Assessment (Ranasinghe et al., 2020)](https://github.com/sheffieldnlp/deepQuest-py/tree/main/examples/monotransquest)
- [Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation (Lee, 2020)](https://github.com/sheffieldnlp/deepQuest-py/tree/main/examples/beringlab)
- [deepQuest: A Framework for Neural-based Quality Estimation (Ive et al., 2018)](https://github.com/sheffieldnlp/deepQuest-py/tree/main/examples/birnn)
See our [examples](https://github.com/sheffieldnlp/deepQuest-py/tree/main/examples) for instructions on how to train and test specific models.
## Online Demo
Check out our [web tool](https://dq.fredblain.org/) to try out most of our trained models on your own data!
## Installation
deepQuest-py requires Python 3.6 or later.
```
git clone https://github.com/sheffieldnlp/deepQuest-py.git
cd deepQuest-py
pip install -e .
```
## Licence
deepQuest-py is licenced under a CC BY-NC-SA licence.
## Citation
If you use deepQuest-py in your research, please cite our [EMNLP 2021 Demo paper](https://aclanthology.org/2021.emnlp-demo.42.pdf):
```
@inproceedings{alva-manchego-etal-2021-deepquest,
title = "deep{Q}uest-py: {L}arge and Distilled Models for Quality Estimation",
author = "Alva-Manchego, Fernando and
Obamuyide, Abiola and
Gajbhiye, Amit and
Blain, Fr{\'e}d{\'e}ric and
Fomicheva, Marina and
Specia, Lucia",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-demo.42",
pages = "382--389",
}
```