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README.md

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+---
+task_categories:
+- visual-question-answering
+- table-question-answering
+language:
+- ja
+tags:
+- table-qa
+- visual-qa
+- japanese
+- ntcir
+size_categories:
+- 10K<n<100K
+---
+
+# TableCellQA Dataset
+
+This dataset is for Table Question Answering (Table QA), derived from tables in Japanese annual securities reports used in the NTCIR-18 U4 shared task. 
+
+This dataset was proposed in our paper: [Enhancing Large Vision-Language Models with Layout Modality for Table Question Answering on Japanese Annual Securities Reports](https://arxiv.org/abs/2505.17625).
+
+## Key Differences from Original Dataset
+
+1. **Multimodal Support**: This dataset supports multimodal inputs (image, layout, text) for comprehensive table understanding
+2. **Direct Cell Value Extraction**: Unlike the original task, this dataset focuses on direct extraction of cell values, removing the need for arithmetic operations or other transformations
+
+## Dataset Description
+
+- **Language**: Japanese
+- **Task**: Table Question Answering
+- **Format**: Images with OCR text and question-answer pairs
+- **Source**: NTCIR-18 U4 Task
+
+## Dataset Structure
+
+Each example contains:
+- `id`: Unique identifier
+- `sample_id`: Original sample ID
+- `image`: Table image (PNG format)
+- `text_w_bbox`: Raw OCR data with bounding box information (JSON format)
+- `question`: Question about the table
+- `answer`: Answer to the question
+- `question_type`: Type of question (table_qa)
+- `dataset`: Dataset name (ntcir18-u4)
+
+## Usage
+
+```python
+from datasets import load_dataset
+import json
+
+dataset = load_dataset("stockmark/u4-table-cell-qa")
+
+# Access OCR data with bounding boxes
+sample = dataset["train"][0]
+ocr_data = json.loads(sample["text_w_bbox"])
+
+# Each OCR element contains:
+# - "box": [x1, y1, x2, y2] - bounding box coordinates
+# - "text": extracted text
+# - "label": classification label (if available)
+# - "words": word-level information (if available)
+
+for ocr_item in ocr_data:
+    print(f"Text: {ocr_item['text']}")
+    print(f"Box: {ocr_item['box']}")
+```
+
+## Citation
+
+### Original Dataset
+
+This dataset is based on the NTCIR-U4 task. We thank the original authors for making their data available.
+
+**Data Source:**
+- 本データは金融庁 EDINET で公開されている有価証券報告書を基に編集・加工したものです。
+- This data is based on securities reports published on EDINET (Financial Services Agency of Japan), which have been edited and processed.
+
+**Attribution:**
+- 出典：EDINET（金融庁）/ Source: EDINET (Financial Services Agency of Japan)
+- 編集・加工：ストックマーク株式会社（NTCIR-18 U4 タスク関連データ）/ Edited and processed by: Stockmark Inc. (NTCIR-18 U4 Task related data)
+
+**References:**
+- Task Overview: https://sites.google.com/view/ntcir18-u4/
+- Data and Code (GitHub): https://github.com/nlp-for-japanese-securities-reports/ntcir18-u4
+
+```bibtex
+@article{EMTCIR2024,
+ title  = {Understanding Tables in Financial Documents: Shared Tasks for Table Retrieval and Table QA on Japanese Annual Security Reports},
+ author = {Yasutomo Kimura and Eisaku Sato and Kazuma Kadowaki and Hokuto Ototake},
+ journal = {Proceedings of the SIGIR-AP 2024 Workshops EMTCIR 2024},
+ month  = {12},
+ year  = {2024},
+ url   = {https://ceur-ws.org/Vol-3854/}
+}
+```
+
+### Our Paper
+
+If you use this dataset, please cite our paper:
+
+```bibtex
+@article{aida2025enhancinglargevisionlanguagemodels,
+      title={Enhancing Large Vision-Language Models with Layout Modality for Table Question Answering on Japanese Annual Securities Reports}, 
+      author={Hayato Aida and Kosuke Takahashi and Takahiro Omi},
+      year={2025},
+      eprint={2505.17625},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2505.17625}, 
+}
+```
+
+### This Dataset
+
+If you use this processed dataset, please also cite:
+
+```bibtex
+@dataset{table_cell_qa_2025,
+  title={TableCellQA Dataset},
+  author={Hayato Aida},
+  year={2025},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/your-username/ntcir-u4-table-qa}
+}
+```

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