license: cc-by-sa-4.0
dataset_info:
features:
- name: id
dtype: string
- name: lp
dtype: string
- name: source
dtype: string
- name: reference
dtype: string
- name: gold_clean
dtype: string
- name: translation
dtype: string
- name: src_lang
dtype: string
- name: tgt_lang
dtype: string
- name: label
dtype: int64
- name: noise_category
dtype: string
- name: noise_pattern
dtype: string
- name: split
dtype: string
splits:
- name: train
num_bytes: 9755128
num_examples: 8800
download_size: 3139078
dataset_size: 9755128
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
language:
- en
- ar
- zh
- he
- fr
- ru
- it
- de
- ko
- cs
- pl
- ta
- km
Dataset Summary
Name: TransClean — A benchmark for detecting and extracting clean translations from LLM outputs.
Description: This dataset contains synthetic examples used to evaluate methods for detecting and extracting clean translations produced by large language models. It supports research on translation quality estimation, extraction, and filtering of clean model outputs. More details can be found in our paper at WMT26.
Data columns: translation is the output generated by LLMs, which might contain noise if directly used for evaluation. Hence, it is the input of noise extraction method to get clean translation. gold_clean is the gold clean translation that can be used for computing accuracy metric. noise_pattern indicates what kind of noise the translation contains. When noise_pattern is none, it mean it's already a clean translation. When it is off_topic, then there is no clean translation to be extracted, hence an empty string.
Included files: synthetic.jsonl for the synthetic subset only. Please contact us if you need the curated subset curated_noisy_1100_with_silver.jsonl.