https://github.com/unbabel/kiwicutter

KiwiCutter is a simple introduction to using OpenKiwi
https://github.com/unbabel/kiwicutter

Last synced: 6 months ago
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KiwiCutter is a simple introduction to using OpenKiwi

• Host: GitHub
• URL: https://github.com/unbabel/kiwicutter
• Owner: Unbabel
• License: agpl-3.0
• Created: 2019-09-11T13:24:12.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2022-12-08T06:10:33.000Z (over 3 years ago)
• Last Synced: 2023-08-07T02:10:58.244Z (almost 3 years ago)
• Language: Mathematica
• Size: 1.96 MB
• Stars: 13
• Watchers: 20
• Forks: 5
• Open Issues: 18
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
![OpenKiwi Logo](https://github.com/Unbabel/OpenKiwi/blob/master/docs/_static/img/openkiwi-logo-horizontal.svg)

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# KiwiCutter

KiwiCutter is an easy-to-use tutorial for OpenKiwi. This was originally presented at the [MT Marathon](https://github.com/EdinburghNLP/mtm19) in Edinburgh.

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.

OpenKiwi, is 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.

Furthermore, we built on top of this framework to win the WMT 2019 shared task on quality estimation. You can check our approach [here](https://www.aclweb.org/anthology/P19-3020)

## Overview of the Tutorial

We are going to split the tutorial in two parts:

* Interactive usage of Kiwi using a Jupyter notebook

* Ideas for practical exercises to learn how to develop and make modifications on Kiwi

You can find the notebook in this repo and the description of the exercises under the `exercise` folder.