license: apache-2.0
size_categories:
- 1M<n<10M
language:
- cs
- de
- en
- hr
- ja
- liv
- ru
- sah
- uk
- zh
tags:
- mt-evaluation
- WMT
- 12-lang-pairs
Dataset Summary
In 2022, several changes were made to the annotation procedure used in the WMT Translation task. In contrast to the standard DA (sliding scale from 0-100) used in previous years, in 2022 annotators performed DA+SQM (Direct Assessment + Scalar Quality Metric). In DA+SQM, the annotators still provide a raw score between 0 and 100, but also are presented with seven labeled tick marks. DA+SQM helps to stabilize scores across annotators (as compared to DA).
The data is organised into 8 columns:
- lp: language pair
- src: input text
- mt: translation
- ref: reference translation
- score: direct assessment
- system: MT engine that produced the
mt - annotators: number of annotators
- domain: domain of the input text (e.g. news)
- year: collection year
You can also find the original data here
Python usage:
from datasets import load_dataset
dataset = load_dataset("RicardoRei/wmt-sqm-human-evaluation", split="train")
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, so far, all data is from 2022 General Translation task
Citation Information
If you use this data please cite the WMT findings: