KiwiCutter

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KiwiCutter is a simple introduction to using OpenKiwi

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![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.

KiwiCutter

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