ymoslem/wmt-da-human-evaluation

Dataset Summary

This dataset contains all DA human annotations from previous WMT News Translation shared tasks. It is a copy of this dataset RicardoRei/wmt-da-human-evaluation, split into train and test.

DatasetDict({
    train: Dataset({
        features: ['lp', 'src', 'mt', 'ref', 'score', 'raw', 'annotators', 'domain', 'year'],
        num_rows: 1275190
    })
    test: Dataset({
        features: ['lp', 'src', 'mt', 'ref', 'score', 'raw', 'annotators', 'domain', 'year'],
        num_rows: 10000
    })
})

Moreover, the raw column is normalized to be between 0 and 1 using this function.

def normalize_score(example):
    example["raw"] = example["raw"] / 100.0
    return example

dataset = dataset.map(normalize_score)

The data is organised into 8 columns:

• lp: language pair
• src: input text
• mt: translation
• ref: reference translation
• score: z score
• raw: direct assessment
• annotators: number of annotators
• domain: domain of the input text (e.g. news)
• year: collection year

You can also find the original data for each year in the results section https://www.statmt.org/wmt{YEAR}/results.html e.g: for 2020 data: https://www.statmt.org/wmt20/results.html

Python usage:

from datasets import load_dataset
dataset = load_dataset("ymoslem/wmt-da-human-evaluation")

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.:

# split by year
data = dataset.filter(lambda example: example["year"] == 2022)

# split by LP
data = dataset.filter(lambda example: example["lp"] == "en-de")

# split by domain
data = dataset.filter(lambda example: example["domain"] == "news")

Note that most data is from News domain.

Citation Information

If you use this data please cite the WMT findings from previous years:

• Findings of the 2017 Conference on Machine Translation (WMT17)
• Findings of the 2018 Conference on Machine Translation (WMT18)
• Findings of the 2019 Conference on Machine Translation (WMT19)
• Findings of the 2020 Conference on Machine Translation (WMT20)
• Findings of the 2021 Conference on Machine Translation (WMT21)
• Findings of the 2022 Conference on Machine Translation (WMT22)