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---
dataset_info:
features:
- name: lp
dtype: large_string
- name: src
dtype: large_string
- name: mt
dtype: large_string
- name: ref
dtype: large_string
- name: raw
dtype: float64
- name: domain
dtype: large_string
- name: year
dtype: int64
- name: sents
dtype: int32
splits:
- name: train
num_bytes: 36666470784
num_examples: 7650287
- name: test
num_bytes: 283829719
num_examples: 59235
download_size: 23178699933
dataset_size: 36950300503
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
license: apache-2.0
size_categories:
- 1M<n<10M
language:
- bn
- cs
- de
- en
- et
- fi
- fr
- gu
- ha
- hi
- is
- ja
- kk
- km
- lt
- lv
- pl
- ps
- ru
- ta
- tr
- uk
- xh
- zh
- zu
tags:
- mt-evaluation
- WMT
- 41-lang-pairs
---
# Dataset Summary
**Long-context / document-level** dataset for Quality Estimation of Machine Translation.
It is an augmented variant of the sentence-level WMT DA Human Evaluation dataset.
In addition to individual sentences, it contains augmentations of 2, 4, 8, 16, and 32 sentences, among each language pair `lp` and `domain`.
The `raw` column represents a weighted average of scores of augmented sentences using character lengths of `src` and `mt` as weights.
The code used to apply the augmentation can be found [here](https://github.com/ymoslem/datasets/blob/main/LongContextQE/Long-Context-MT-QE-WMT.ipynb).
This dataset contains all DA human annotations from previous WMT News Translation shared tasks.
It extends the sentence-level dataset [RicardoRei/wmt-da-human-evaluation](https://huggingface.co/datasets/RicardoRei/wmt-da-human-evaluation), split into `train` and `test`.
Moreover, the `raw` column is normalized to be between 0 and 1 using this function.
The data is organised into 8 columns:
- lp: language pair
- src: input text
- mt: translation
- ref: reference translation
- raw: direct assessment
- domain: domain of the input text (e.g. news)
- year: collection year
- sents: number of sentences in the text
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](https://www.statmt.org/wmt20/results.html)
## Python usage:
```python
from datasets import load_dataset
dataset = load_dataset("ymoslem/wmt-da-human-evaluation-long-context")
```
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.:
```python
# 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 the 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)](https://aclanthology.org/W17-4717.pdf)
- [Findings of the 2018 Conference on Machine Translation (WMT18)](https://aclanthology.org/W18-6401.pdf)
- [Findings of the 2019 Conference on Machine Translation (WMT19)](https://aclanthology.org/W19-5301.pdf)
- [Findings of the 2020 Conference on Machine Translation (WMT20)](https://aclanthology.org/2020.wmt-1.1.pdf)
- [Findings of the 2021 Conference on Machine Translation (WMT21)](https://aclanthology.org/2021.wmt-1.1.pdf)
- [Findings of the 2022 Conference on Machine Translation (WMT22)](https://aclanthology.org/2022.wmt-1.1.pdf) |