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• HistMTQE – a human-annotated subset of historical documents (English–Chinese and Russian–Chinese), reflecting real-world noisy and degraded text image conditions.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.

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📁 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)

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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.

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Contains historical document test sets with human-annotated quality scores: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

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🔄 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.