Post-editing and MQM annotations produced by the QT21 project. As described in
@InProceedings{specia-etal_MTSummit:2017,
author = {Specia, Lucia and Kim Harris and Frédéric Blain and Aljoscha Burchardt and Viviven Macketanz and Inguna Skadiņa and Matteo Negri and and Marco Turchi},
title = {Translation Quality and Productivity: A Study on Rich Morphology Languages},
booktitle = {Proceedings of Machine Translation Summit XVI},
year = {2017},
pages = {55--71},
address = {Nagoya, Japan},
}
Test data for the WMT 2017 Automatic post-editing task (the same used for the Sentence-level Quality Estimation task). They consist in German-English triplets (source and target) belonging to the pharmacological domain and already tokenized. Test set contains 2,000 pairs. All data is provided by the EU project QT21 (http://www.qt21.eu/).
Test data for the WMT 2017 Automatic post-editing task (the same used for the Sentence-level Quality Estimation task). They consist in 2,000 English-German pairs (source and target) belonging to the IT domain and already tokenized. All data is provided by the EU project QT21 (http://www.qt21.eu/).
Test data for the WMT 2018 Automatic post-editing task. They consist in English-German pairs (source and target) belonging to the information technology domain and already tokenized. Test set contains 1,023 pairs. A neural machine translation system has been used to generate the target segments. All data is provided by the EU project QT21 (http://www.qt21.eu/).
Test data for the WMT 2018 Automatic post-editing task. They consist in English-German pairs (source and target) belonging to the information technology domain and already tokenized. Test set contains 2,000 pairs. A phrase-based machine translation system has been used to generate the target segments. This test set is sampled from the same dataset used for the 2016 and 2017 APE shared task editions. All data is provided by the EU project QT21 (http://www.qt21.eu/).
Training, development and text data (the same used for the Sentence-level Quality Estimation task) consist in English-German triplets (source, target and post-edit) belonging to the IT domain and already tokenized.
Training and development respectively contain 12,000 and 1,000 triplets, while the test set 2,000 instances. All data is provided by the EU project QT21 (http://www.qt21.eu/).
Training and development data for the WMT16 QE task. Test data will be published as a separate item.
This shared task will build on its previous four editions to further examine automatic methods for estimating the quality of machine translation output at run-time, without relying on reference translations. We include word-level, sentence-level and document-level estimation. The sentence and word-level tasks will explore a large dataset produced from post-editions by professional translators (as opposed to crowdsourced translations as in the previous year). For the first time, the data will be domain-specific (IT domain). The document-level task will use, for the first time, entire documents, which have been human annotated for quality indirectly in two ways: through reading comprehension tests and through a two-stage post-editing exercise. Our tasks have the following goals:
- To advance work on sentence and word-level quality estimation by providing domain-specific, larger and professionally annotated datasets.
- To study the utility of detailed information logged during post-editing (time, keystrokes, actual edits) for different levels of prediction.
- To analyse the effectiveness of different types of quality labels provided by humans for longer texts in document-level prediction.
This year's shared task provides new training and test datasets for all tasks, and allows participants to explore any additional data and resources deemed relevant. A in-house MT system was used to produce translations for the sentence and word-level tasks, and multiple MT systems were used to produce translations for the document-level task. Therefore, MT system-dependent information will be made available where possible.
Training and development data for the WMT 2017 Automatic post-editing task (the same used for the Sentence-level Quality Estimation task). They consist in German-English triplets (source, target and post-edit) belonging to the pharmacological domain and already tokenized. Training and development respectively contain 25,000 and 1,000 triplets. All data is provided by the EU project QT21 (http://www.qt21.eu/).
Training data for the WMT 2017 Automatic post-editing task (the same used for the Sentence-level Quality Estimation task). They consist in 11,000 English-German triplets (source, target and post-edit) belonging to the IT domain and already tokenized. All data is provided by the EU project QT21 (http://www.qt21.eu/).
Training and development data for the WMT17 QE task. Test data will be published as a separate item.
This shared task will build on its previous five editions to further examine automatic methods for estimating the quality of machine translation output at run-time, without relying on reference translations. We include word-level, phrase-level and sentence-level estimation. All tasks will make use of a large dataset produced from post-editions by professional translators. The data will be domain-specific (IT and Pharmaceutical domains) and substantially larger than in previous years. In addition to advancing the state of the art at all prediction levels, our goals include:
- To test the effectiveness of larger (domain-specific and professionally annotated) datasets. We will do so by increasing the size of one of last year's training sets.
- To study the effect of language direction and domain. We will do so by providing two datasets created in similar ways, but for different domains and language directions.
- To investigate the utility of detailed information logged during post-editing. We will do so by providing post-editing time, keystrokes, and actual edits.
This year's shared task provides new training and test datasets for all tasks, and allows participants to explore any additional data and resources deemed relevant. A in-house MT system was used to produce translations for all tasks. MT system-dependent information can be made available under request. The data is publicly available but since it has been provided by our industry partners it is subject to specific terms and conditions. However, these have no practical implications on the use of this data for research purposes.