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--- |
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license: apache-2.0 |
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language: |
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- en |
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- de |
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- ru |
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- zh |
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tags: |
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- mt-evaluation |
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- WMT |
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- MQM |
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size_categories: |
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- 100K<n<1M |
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--- |
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# Dataset Summary |
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This dataset contains all MQM human annotations from previous [WMT Metrics shared tasks](https://wmt-metrics-task.github.io/) and the MQM annotations from [Experts, Errors, and Context](https://aclanthology.org/2021.tacl-1.87/) in a form of error spans. Moreover, it contains some hallucinations used in the training of [XCOMET models](https://huggingface.co/Unbabel/XCOMET-XXL). |
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**Please note that this is not an official release of the data** and the original data can be found [here](https://github.com/google/wmt-mqm-human-evaluation). |
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The data is organised into 8 columns: |
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- src: input text |
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- mt: translation |
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- ref: reference translation |
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- annotations: List of error spans (dictionaries with 'start', 'end', 'severity', 'text') |
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- lp: language pair |
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While `en-ru` was annotated by Unbabel, `en-de` and `zh-en` was annotated by Google. This means that for en-de and zh-en you will only find minor and major errors while for en-ru you can find a few critical errors. |
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## Python usage: |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("RicardoRei/wmt-mqm-error-spans", split="train") |
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``` |
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There is no standard train/test split for this dataset but you can easily split it according to year, language pair or domain. E.g. : |
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```python |
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# split by LP |
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data = dataset.filter(lambda example: example["lp"] == "en-de") |
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``` |
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## Citation Information |
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If you use this data please cite the following works: |
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- [Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation](https://aclanthology.org/2021.tacl-1.87/) |
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- [Results of the WMT21 Metrics Shared Task: Evaluating Metrics with Expert-based Human Evaluations on TED and News Domain](https://aclanthology.org/2021.wmt-1.73/) |
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- [Results of WMT22 Metrics Shared Task: Stop Using BLEU – Neural Metrics Are Better and More Robust](https://aclanthology.org/2022.wmt-1.2/) |
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- [xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detection](https://arxiv.org/pdf/2310.10482.pdf) |
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