alphadl/openkiwi

Open-Source Machine Translation Quality Estimation in PyTorch

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Open-Source Machine Translation Quality Estimation in PyTorch

Quality estimation (QE) is one of the missing pieces of machine translation: its goal is to evaluate a translation system’s quality without access to reference translations. We present OpenKiwi, a Pytorch-based open-source framework that implements the best QE systems from WMT 2015-18 shared tasks, making it easy to experiment with these models under the same framework. Using OpenKiwi and a stacked combination of these models we have achieved state-of-the-art results on word-level QE on the WMT 2018 English-German dataset.

News

Following our nomination in early July, we are happy to announce we won the Best Demo Paper at ACL 2019! Congratulations to the whole team and huge thanks for supporters and issue reporters.

Check out the published paper.

We are going to release the web interface we had put in place for the live demo presentation at ACL.

We have released the OpenKiwi tutorial we presented at MT Marathon 2019.

Features

• Framework for training QE models and using pre-trained models for evaluating MT.
• Supports both word and sentence-level Quality estimation.
• Implementation of five QE systems in Pytorch: QUETCH [1], NuQE [2, 3], predictor-estimator [4, 5], APE-QE [3], and a stacked ensemble with a linear system [2, 3].
• Easy to use API. Import it as a package in other projects or run from the command line.
• Provides scripts to run pre-trained QE models on data from the WMT 2018 campaign.
• Easy to track and reproduce experiments via yaml configuration files.

Results

Results for the WMT18 Quality Estimation shared task, for word level and sentence level on the test set.

Model	En-De SMT					En-De NMT
	MT	gaps	source	r	⍴	MT	gaps	source	r	⍴
OpenKiwi	62.70	52.14	48.88	71.08	72.70	44.77	22.89	36.53	46.72	58.51
Wang2018	62.46	49.99	--	73.97	75.43	43.61	--	--	50.12	60.49
UNQE	--	--	--	70.00	72.44	--	--	--	51.29	60.52
deepQUEST	42.98	28.24	33.97	48.72	50.97	30.31	11.93	28.59	38.08	48.00

Quick Installation

To install OpenKiwi as a package, simply run

pip install openkiwi

You can now

import kiwi

inside your project or run in the command line

kiwi

Optionally, if you'd like to take advantage of our MLflow integration, simply install it in the same virtualenv as OpenKiwi:

pip install mlflow

Getting Started

Detailed usage examples and instructions can be found in the Full Documentation.

Pre-trained models

We provide pre-trained models with the corresponding pre-processed datasets and configuration files. You can easily reproduce our numbers in the WMT 2018 word- and sentence-level tasks by following the reproduce instructions in the documentation.

Contributing

We welcome contributions to improve OpenKiwi. Please refer to CONTRIBUTING.md for quick instructions or to contributing instructions for more detailed instructions on how to set up your development environment.

License

OpenKiwi is Affero GPL licensed. You can see the details of this license in LICENSE.

Citation

If you use OpenKiwi, please cite the following paper: OpenKiwi: An Open Source Framework for Quality Estimation.

@inproceedings{openkiwi,
    author = {Fábio Kepler and
              Jonay Trénous and
              Marcos Treviso and
              Miguel Vera and
              André F. T. Martins},
    title  = {Open{K}iwi: An Open Source Framework for Quality Estimation},
    year   = {2019},
    booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics--System Demonstrations},
    pages  = {117--122},
    month  = {July},
    address = {Florence, Italy},
    url    = {https://www.aclweb.org/anthology/P19-3020},
    organization = {Association for Computational Linguistics},
}

References

[1] Kreutzer et al. (2015): QUality Estimation from ScraTCH (QUETCH): Deep Learning for Word-level Translation Quality Estimation

[2] Martins et al. (2016): Unbabel's Participation in the WMT16 Word-Level Translation Quality Estimation Shared Task

[3] Martins et al. (2017): Pushing the Limits of Translation Quality Estimation

[4] Kim et al. (2017): Predictor-Estimator using Multilevel Task Learning with Stack Propagation for Neural Quality Estimation

[5] Wang et al. (2018): Alibaba Submission for WMT18 Quality Estimation Task