zouhar/wmt-human-all
This dataset is continuously updated and contains a compilation of human translation quality assessment from past WMT campaigns. Specifically, this dataset merges all annotation protocols (DA, MQM, ESA) on a semi-unified scale (0 to 100). The current version of the dataset includes human scores up to WMT 2025 (inclusive) and has been created with the following script: import subset2evaluate # version 1.0.20 import json import statistics data =… See the full description on the dataset page: https://huggingface.co/datasets/zouhar/wmt-human-all.
This dataset is continuously updated and contains a compilation of human translation quality assessment from past WMT campaigns. Specifically, this dataset merges all annotation protocols (DA, MQM, ESA) on a semi-unified scale (0 to 100). The current version of the dataset includes human scores up to WMT 2025 (inclusive) and has been created with the following script:
import subset2evaluate # version 1.0.20
import json
import statistics
data = subset2evaluate.utils.load_data_wmt_all(min_items=10, include_ref=True)
# flatten
data = [
{
"src": line["src"],
"mt": line["tgt"][sys],
"score": line["scores"][sys]["human"],
"dataset": "/".join(data_name),
}
for data_name, data_v in data.items()
for line in data_v
for sys in line["scores"].keys()
]
# only one split, filter by "dataset" keyword if you want to
with open("train.jsonl", "w") as f:
for line in data:
f.write(json.dumps(line) + "\n")
# plot score distribution
import matplotlib.pyplot as plt
plt.hist([x["score"] for x in data], density=True, bins=30, color="black")
plt.gca().spines[["top", "right"]].set_visible(False)
plt.ylabel("Frequency")
plt.xlabel("Score")
plt.show()
print(len(data)) # 769328 up to WMT2025
print(sorted({x for line in data for x in line["dataset"].split("/")[1].split("-")}))<!-- huggingface-cli upload --repo-type dataset zouharvi/wmt-human-all . . -->
You can use the dataset as:
import datasets
datasets.load_dataset("zouharvi/wmt-human-all", split="train") <img src="https://cdn-uploads.huggingface.co/production/uploads/6304ece07424d937fa35fb98/lKaq2c9glgRtdLR4trFY7.png" width="400px">
If you use this dataset, cite the latest WMT findings paper:
@inproceedings{kocmi-etal-2025-findings,
title = "Findings of the {WMT}25 General Machine Translation Shared Task: Time to Stop Evaluating on Easy Test Sets",
author = "Kocmi, Tom and
Artemova, Ekaterina and
Avramidis, Eleftherios and
Bawden, Rachel and
Bojar, Ond{\v{r}}ej and
Dranch, Konstantin and
Dvorkovich, Anton and
Dukanov, Sergey and
Fishel, Mark and
Freitag, Markus and
Gowda, Thamme and
Grundkiewicz, Roman and
Haddow, Barry and
Karpinska, Marzena and
Koehn, Philipp and
Lakougna, Howard and
Lundin, Jessica and
Monz, Christof and
Murray, Kenton and
Nagata, Masaaki and
Perrella, Stefano and
Proietti, Lorenzo and
Popel, Martin and
Popovi{\'c}, Maja and
Riley, Parker and
Shmatova, Mariya and
Steingr{\'i}msson, Steinth{\'o}r and
Yankovskaya, Lisa and
Zouhar, Vil{\'e}m",
editor = "Haddow, Barry and
Kocmi, Tom and
Koehn, Philipp and
Monz, Christof",
booktitle = "Proceedings of the Tenth Conference on Machine Translation",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.wmt-1.22/",
doi = "10.18653/v1/2025.wmt-1.22",
pages = "355--413",
ISBN = "979-8-89176-341-8",
abstract = "This paper presents the results of the General Machine Translation Task organized as part of the 2025 Conference on Machine Translation (WMT). Participants were invited to build systems for any of 30 language pairs. For half of these pairs, we conducted a human evaluation on test sets spanning four to five different domains.We evaluated 60 systems in total: 36 submitted by participants and 24 for which we collected translations from large language models (LLMs) and popular online translation providers.This year, we focused on creating challenging test sets by developing a difficulty sampling technique and using more complex source data. We evaluated system outputs with professional annotators using the Error Span Annotation (ESA) protocol, except for two language pairs, for which we used Multidimensional Quality Metrics (MQM) instead.We continued the trend of increasingly moving towards document-level translation, providing the source texts as whole documents containing multiple paragraphs."
}
@inproceedings{kocmi-etal-2024-findings,
title = "Findings of the {WMT}24 General Machine Translation Shared Task: The {LLM} Era Is Here but {MT} Is Not Solved Yet",
author = "Kocmi, Tom and
Avramidis, Eleftherios and
Bawden, Rachel and
Bojar, Ond{\v{r}}ej and
Dvorkovich, Anton and
Federmann, Christian and
Fishel, Mark and
Freitag, Markus and
Gowda, Thamme and
Grundkiewicz, Roman and
Haddow, Barry and
Karpinska, Marzena and
Koehn, Philipp and
Marie, Benjamin and
Monz, Christof and
Murray, Kenton and
Nagata, Masaaki and
Popel, Martin and
Popovi{\'c}, Maja and
Shmatova, Mariya and
Steingr{\'i}msson, Steinth{\'o}r and
Zouhar, Vil{\'e}m",
editor = "Haddow, Barry and
Kocmi, Tom and
Koehn, Philipp and
Monz, Christof",
booktitle = "Proceedings of the Ninth Conference on Machine Translation",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.wmt-1.1/",
doi = "10.18653/v1/2024.wmt-1.1",
pages = "1--46",
abstract = "This overview paper presents the results of the General Machine Translation Task organised as part of the 2024 Conference on Machine Translation (WMT). In the general MT task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting of three to five different domains. In addition to participating systems, we collected translations from 8 different large language models (LLMs) and 4 online translation providers. We evaluate system outputs with professional human annotators using a new protocol called Error Span Annotations (ESA)."
}