TIMTQE Benchmark Dataset

This repository provides the TIMTQE benchmark dataset, designed for translation quality estimation (QE) of text images.
TIMTQE consists of two complementary components:

MLQE-PE – a large-scale synthetic dataset derived from MLQE-PE, where source sentences are rendered into text images and paired with translation quality annotations.
HistMTQE – a human-annotated subset of historical documents (English–Chinese and Russian–Chinese), reflecting real-world noisy and degraded text image conditions.

Together, these resources enable benchmarking multimodal, multilingual QE models under both synthetic and historical settings.

📁 Data Organization

The dataset is structured into three main directories:

1. MLQE-PE_jsonl

Contains JSONL files organized by prompt type:

normal/ – default prompt format (used in main experiments)
cot/ – chain-of-thought style prompts
multi-task/ – multi-task prompt format

Each subdirectory includes:

train.json
dev.json
test files (e.g., test_en-de.json)

Fields in each JSONL entry:

Field	Description
`image`	Relative path to the text image file
`text`	Prompt-response conversation (depends on prompt template)
`task_type`	Task identifier (e.g., `llava_sft`)

2. MLQE-PE_image

Contains text image files under different augmentation settings:

pngs/ – default (normal) images
pngs_bleed_through/ – images with bleed-through noise
pngs_skew/ – images with skew distortion
... (9 variants in total, as detailed in the paper)

Each subdirectory represents one type of augmentation applied to MLQE-PE.

3. HistMTQE

Contains historical document test sets with human-annotated quality scores:

HistMTQE_en-zh_test.tsv – English → Chinese test set
HistMTQE_ru-zh_test.tsv – Russian → Chinese test set

TSV fields:

Field	Description
`index`	Sample index
`original`	Source sentence
`translation`	Machine translation output
`scores`	Raw annotation scores from multiple annotators
`mean`	Average score
`z_scores`	Standardized scores
`z_mean`	Average standardized score

🔄 Switching Data Variants

Change prompt template
Select JSONL files from:
- MLQE-PE_jsonl/normal/
- MLQE-PE_jsonl/cot/
- MLQE-PE_jsonl/multi-task/
Change image augmentation
Update the image path in JSONL files to point to the desired subdirectory.