license: cc-by-nc-nd-4.0
pretty_name: 'HAT: Hallucination Annotation for Translation'
size_categories:
- 100K<n<1M
annotations_creators:
- expert-generated
task_categories:
- translation
- text-classification
language:
- ar
- nl
- en
- fr
- de
- hi
- id
- it
- ja
- ko
- pl
- pt
- ru
- zh
- es
- th
- tr
- uk
- vi
tags:
- machine-translation
- hallucination-detection
- mt-evaluation
configs:
- config_name: default
data_files:
- split: train
path: data/train/*/*.parquet
- split: validation
path: data/dev/*/*.parquet
- split: test
path: data/test/*/*.parquet
- config_name: ar_AE-en_US
data_files:
- split: train
path: data/train/ar_AE-en_US/*.parquet
- split: validation
path: data/dev/ar_AE-en_US/*.parquet
- split: test
path: data/test/ar_AE-en_US/*.parquet
- config_name: de_DE-en_US
data_files:
- split: train
path: data/train/de_DE-en_US/*.parquet
- split: validation
path: data/dev/de_DE-en_US/*.parquet
- split: test
path: data/test/de_DE-en_US/*.parquet
- config_name: en_US-ar_AE
data_files:
- split: train
path: data/train/en_US-ar_AE/*.parquet
- split: validation
path: data/dev/en_US-ar_AE/*.parquet
- split: test
path: data/test/en_US-ar_AE/*.parquet
- config_name: en_US-de_DE
data_files:
- split: train
path: data/train/en_US-de_DE/*.parquet
- split: validation
path: data/dev/en_US-de_DE/*.parquet
- split: test
path: data/test/en_US-de_DE/*.parquet
- config_name: en_US-es_ES
data_files:
- split: train
path: data/train/en_US-es_ES/*.parquet
- split: validation
path: data/dev/en_US-es_ES/*.parquet
- split: test
path: data/test/en_US-es_ES/*.parquet
- config_name: en_US-fr_FR
data_files:
- split: train
path: data/train/en_US-fr_FR/*.parquet
- split: validation
path: data/dev/en_US-fr_FR/*.parquet
- split: test
path: data/test/en_US-fr_FR/*.parquet
- config_name: en_US-hi_IN
data_files:
- split: train
path: data/train/en_US-hi_IN/*.parquet
- split: validation
path: data/dev/en_US-hi_IN/*.parquet
- split: test
path: data/test/en_US-hi_IN/*.parquet
- config_name: en_US-id_ID
data_files:
- split: train
path: data/train/en_US-id_ID/*.parquet
- split: validation
path: data/dev/en_US-id_ID/*.parquet
- split: test
path: data/test/en_US-id_ID/*.parquet
- config_name: en_US-it_IT
data_files:
- split: train
path: data/train/en_US-it_IT/*.parquet
- split: validation
path: data/dev/en_US-it_IT/*.parquet
- split: test
path: data/test/en_US-it_IT/*.parquet
- config_name: en_US-ja_JP
data_files:
- split: train
path: data/train/en_US-ja_JP/*.parquet
- split: validation
path: data/dev/en_US-ja_JP/*.parquet
- split: test
path: data/test/en_US-ja_JP/*.parquet
- config_name: en_US-ko_KR
data_files:
- split: train
path: data/train/en_US-ko_KR/*.parquet
- split: validation
path: data/dev/en_US-ko_KR/*.parquet
- split: test
path: data/test/en_US-ko_KR/*.parquet
- config_name: en_US-nl_NL
data_files:
- split: train
path: data/train/en_US-nl_NL/*.parquet
- split: validation
path: data/dev/en_US-nl_NL/*.parquet
- split: test
path: data/test/en_US-nl_NL/*.parquet
- config_name: en_US-pl_PL
data_files:
- split: train
path: data/train/en_US-pl_PL/*.parquet
- split: validation
path: data/dev/en_US-pl_PL/*.parquet
- split: test
path: data/test/en_US-pl_PL/*.parquet
- config_name: en_US-pt_BR
data_files:
- split: train
path: data/train/en_US-pt_BR/*.parquet
- split: validation
path: data/dev/en_US-pt_BR/*.parquet
- split: test
path: data/test/en_US-pt_BR/*.parquet
- config_name: en_US-ru_RU
data_files:
- split: train
path: data/train/en_US-ru_RU/*.parquet
- split: validation
path: data/dev/en_US-ru_RU/*.parquet
- split: test
path: data/test/en_US-ru_RU/*.parquet
- config_name: en_US-th_TH
data_files:
- split: train
path: data/train/en_US-th_TH/*.parquet
- split: validation
path: data/dev/en_US-th_TH/*.parquet
- split: test
path: data/test/en_US-th_TH/*.parquet
- config_name: en_US-tr_TR
data_files:
- split: train
path: data/train/en_US-tr_TR/*.parquet
- split: validation
path: data/dev/en_US-tr_TR/*.parquet
- split: test
path: data/test/en_US-tr_TR/*.parquet
- config_name: en_US-uk_UA
data_files:
- split: train
path: data/train/en_US-uk_UA/*.parquet
- split: validation
path: data/dev/en_US-uk_UA/*.parquet
- split: test
path: data/test/en_US-uk_UA/*.parquet
- config_name: en_US-vi_VN
data_files:
- split: train
path: data/train/en_US-vi_VN/*.parquet
- split: validation
path: data/dev/en_US-vi_VN/*.parquet
- split: test
path: data/test/en_US-vi_VN/*.parquet
- config_name: en_US-zh_CN
data_files:
- split: train
path: data/train/en_US-zh_CN/*.parquet
- split: validation
path: data/dev/en_US-zh_CN/*.parquet
- split: test
path: data/test/en_US-zh_CN/*.parquet
- config_name: en_US-zh_TW
data_files:
- split: train
path: data/train/en_US-zh_TW/*.parquet
- split: validation
path: data/dev/en_US-zh_TW/*.parquet
- split: test
path: data/test/en_US-zh_TW/*.parquet
- config_name: es_ES-en_US
data_files:
- split: train
path: data/train/es_ES-en_US/*.parquet
- split: validation
path: data/dev/es_ES-en_US/*.parquet
- split: test
path: data/test/es_ES-en_US/*.parquet
- config_name: fr_FR-en_US
data_files:
- split: train
path: data/train/fr_FR-en_US/*.parquet
- split: validation
path: data/dev/fr_FR-en_US/*.parquet
- split: test
path: data/test/fr_FR-en_US/*.parquet
- config_name: hi_IN-en_US
data_files:
- split: train
path: data/train/hi_IN-en_US/*.parquet
- split: validation
path: data/dev/hi_IN-en_US/*.parquet
- split: test
path: data/test/hi_IN-en_US/*.parquet
- config_name: id_ID-en_US
data_files:
- split: train
path: data/train/id_ID-en_US/*.parquet
- split: validation
path: data/dev/id_ID-en_US/*.parquet
- split: test
path: data/test/id_ID-en_US/*.parquet
- config_name: it_IT-en_US
data_files:
- split: train
path: data/train/it_IT-en_US/*.parquet
- split: validation
path: data/dev/it_IT-en_US/*.parquet
- split: test
path: data/test/it_IT-en_US/*.parquet
- config_name: ja_JP-en_US
data_files:
- split: train
path: data/train/ja_JP-en_US/*.parquet
- split: validation
path: data/dev/ja_JP-en_US/*.parquet
- split: test
path: data/test/ja_JP-en_US/*.parquet
- config_name: ko_KR-en_US
data_files:
- split: train
path: data/train/ko_KR-en_US/*.parquet
- split: validation
path: data/dev/ko_KR-en_US/*.parquet
- split: test
path: data/test/ko_KR-en_US/*.parquet
- config_name: nl_NL-en_US
data_files:
- split: train
path: data/train/nl_NL-en_US/*.parquet
- split: validation
path: data/dev/nl_NL-en_US/*.parquet
- split: test
path: data/test/nl_NL-en_US/*.parquet
- config_name: pl_PL-en_US
data_files:
- split: train
path: data/train/pl_PL-en_US/*.parquet
- split: validation
path: data/dev/pl_PL-en_US/*.parquet
- split: test
path: data/test/pl_PL-en_US/*.parquet
- config_name: pt_BR-en_US
data_files:
- split: train
path: data/train/pt_BR-en_US/*.parquet
- split: validation
path: data/dev/pt_BR-en_US/*.parquet
- split: test
path: data/test/pt_BR-en_US/*.parquet
- config_name: ru_RU-en_US
data_files:
- split: train
path: data/train/ru_RU-en_US/*.parquet
- split: validation
path: data/dev/ru_RU-en_US/*.parquet
- split: test
path: data/test/ru_RU-en_US/*.parquet
- config_name: th_TH-en_US
data_files:
- split: train
path: data/train/th_TH-en_US/*.parquet
- split: validation
path: data/dev/th_TH-en_US/*.parquet
- split: test
path: data/test/th_TH-en_US/*.parquet
- config_name: tr_TR-en_US
data_files:
- split: train
path: data/train/tr_TR-en_US/*.parquet
- split: validation
path: data/dev/tr_TR-en_US/*.parquet
- split: test
path: data/test/tr_TR-en_US/*.parquet
- config_name: uk_UA-en_US
data_files:
- split: train
path: data/train/uk_UA-en_US/*.parquet
- split: validation
path: data/dev/uk_UA-en_US/*.parquet
- split: test
path: data/test/uk_UA-en_US/*.parquet
- config_name: vi_VN-en_US
data_files:
- split: train
path: data/train/vi_VN-en_US/*.parquet
- split: validation
path: data/dev/vi_VN-en_US/*.parquet
- split: test
path: data/test/vi_VN-en_US/*.parquet
- config_name: zh_CN-en_US
data_files:
- split: train
path: data/train/zh_CN-en_US/*.parquet
- split: validation
path: data/dev/zh_CN-en_US/*.parquet
- split: test
path: data/test/zh_CN-en_US/*.parquet
- config_name: zh_TW-en_US
data_files:
- split: train
path: data/train/zh_TW-en_US/*.parquet
- split: validation
path: data/dev/zh_TW-en_US/*.parquet
- split: test
path: data/test/zh_TW-en_US/*.parquet
HAT: Hallucination Annotation for Translation
π§ Table of Contents
- Overview
- Usage
- Data Creation Process
- Data Statistics
- Dataset Structure
- Paper Abstract
- Citation
- License
π Overview
HAT (Hallucination Annotation for Translation) is a large-scale dataset for hallucination detection in machine translation (MT).
It is released as part of our publication at ACL 2026 (paper).
- 350,959 span-level annotated samples
- 38 language pairs
- ~8,000-10,000 samples per language pair, divided into train, dev, and test sets
- Annotations by professional translators under strict quality control
HAT enables research on detecting and mitigating hallucinations in MT systems.
π Usage
Load the dataset with the π€ datasets library:
from datasets import load_dataset
# All 38 language pairs
ds = load_dataset("apple/hat")
train, validation, test = ds["train"], ds["validation"], ds["test"]
# A single language pair (e.g. English β Japanese)
ds = load_dataset("apple/hat", "en_US-ja_JP")
The available config names are the 38 language pairs (e.g. en_US-ja_JP, fr_FR-en_US); the
default config concatenates all pairs. Each config exposes train, validation, and test splits.
βοΈ Data Creation Process
The dataset was created through the following steps:
Data Collection:
Crawled ~5M sentences per language from the web, filtered by language ID, length, and deduplication.Translation:
Translated monolingual sentences into target languages using a strong neural MT model.Sample Selection:
Selected translations with lower quality (based on quality metrics) to increase the likelihood of hallucinations.Annotation:
Professional translators labeled hallucinations under rigorous quality control.Post-processing:
Removed samples with issues in source text to ensure data integrity.
π Data Statistics
| Split | Samples per language pair |
|---|---|
| Train | ~10,000 |
| Dev | ~2,000 |
| Test | ~3,000 |
π Dataset Structure
Datasets are organized in the data/ directory:
data/
βββ train/
βββ dev/
βββ test/
Each split contains subdirectories for each language pair which contains one parquet file.
Split naming: the parquet files live under
data/train,data/dev, anddata/test. On the Hugging Face Hub thedevfiles are exposed as the standardvalidationsplit, soload_dataset(...)returnstrain/validation/test.
Schema:
| Field | Type | Description |
|---|---|---|
source_locale |
string |
Locale of the source text |
source_text |
string |
Source sentence |
target_locale |
string |
Locale of the target text |
target_text |
string |
Machine-translated output |
split |
string |
Dataset split (train / dev / test) |
label |
int64 |
Binary hallucination label (0: no hallucination, 1: contains hallucination) |
score |
float64 |
Proportion (0β1) of hallucinated characters |
annotation |
string |
Raw span-level hallucination annotation |
π Paper Abstract
Hallucinations in machine translation (MT)βoutputs that may be fluent yet unfaithful to the source contentβremain a critical obstacle. They hinder the reliable deployment of MT systems in real-world applications. Despite growing attention to this phenomenon, progress has been constrained by the lack of large-scale, high-quality benchmarks dedicated to hallucination detection. We introduce HAT (Hallucination Annotation for Translation), a novel dataset designed to advance research on this problem. HAT comprises 350,959 span-level annotated samples across 38 language pairs, with approximately 8,000β10,000 samples per pair partitioned into training, development, and test sets. Annotations were produced by professional translators under rigorous quality control protocols to ensure reliability. We provide a detailed analysis of hallucination distributions and establish benchmark performance using a diverse set of baselines, including automatic MT evaluation metrics as well as large language models. By providing the first large-scale, systematically annotated resource for hallucination detection in MT, HAT enables the development of more faithful translation models and lays the groundwork for future research on building trustworthy machine translation systems.
π Citation
If you use this dataset, please cite:
@inproceedings{chatterjee-etal-2026-hat,
title = "{HAT}: Hallucination Annotation for Translation",
author = "Chatterjee, Rajen and
Li, Xintong and
Charoenpornsawat, Paisarn and
Lee, Allen",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.721/",
pages = "15865--15888",
ISBN = "979-8-89176-390-6",
}
π License
The HAT dataset is licensed under CC BY-NC-ND 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/).