|
--- |
|
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) |
