Initial commit: DREAM dataset v1.0.0

.gitattributes

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+# all files in this repo that match go through git‑lfs
+*.json  filter=lfs diff=lfs merge=lfs -text
+*.jsonl filter=lfs diff=lfs merge=lfs -text
+*.csv   filter=lfs diff=lfs merge=lfs -text
+data/** filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# ---- ignore only junk inside this dataset repo ----
+__pycache__/
+.ipynb_checkpoints/
+.DS_Store
+.vscode/
+*.tmp
+*.bak
+data/cache/
+data/tmp/

README.md

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+---
+# ====== YAML metadata for the Hub ======
+pretty_name: DREAM-CFB
+license: mit
+language:
+  - en
+tags:
+  - multiple-choice
+  - reading-comprehension
+  - dialogue
+  - conversational-ai
+  - question-answering
+  - openai-format
+task_categories:
+  - question-answering
+size_categories:
+  - 1K<n<10K
+source_datasets:
+  - dream
+annotations_creators:
+  - expert-generated
+---
+
+# DREAM‑CFB · _Dialogue-based Reading Comprehension Examination through Machine Reading (Conversation Fact Benchmark Format)_
+
+**DREAM‑CFB** is a 6,444 example dataset derived from the original **DREAM** dataset, transformed and adapted for the Conversation Fact Benchmark framework. Each item consists of multi-turn dialogues with associated multiple-choice questions that test reading comprehension and conversational understanding.
+
+The dataset focuses on **dialogue-based reading comprehension**: questions require understanding conversational context, speaker intentions, and implicit information that emerges through multi-turn interactions.
+
+The dataset follows a structured format with dialogue turns and questions, making it suitable for evaluating conversational AI systems and reading comprehension models.
+
+---
+
+## Dataset at a glance
+
+| Field                  | Type / shape          | Description                                          |
+| ---------------------- | --------------------- | ---------------------------------------------------- |
+| `id`                   | `str`                 | Unique identifier for the dialogue instance          |
+| `dialogue_turns`       | `list[dict]`          | Multi-turn conversation with speaker and text fields |
+| `questions`            | `list[dict]`          | List of questions associated with the dialogue       |
+| `question_text`        | `str`                 | The comprehension question about the dialogue        |
+| `answer_text`          | `str`                 | Ground-truth answer string                           |
+| `choices`              | `list[str]` (len = 3) | Three multiple-choice answer options                 |
+| `correct_choice_index` | `int` (0‑2)           | Index of the correct answer (0-based)                |
+
+---
+
+## Intended uses
+
+| Use case                     | How to use it                                                   |
+| ---------------------------- | --------------------------------------------------------------- |
+| Reading comprehension eval   | Test model's ability to understand dialogue context and meaning |
+| Conversational understanding | Evaluate comprehension of multi-turn speaker interactions       |
+| Multiple-choice QA           | Assess reasoning capabilities in structured question formats    |
+| Dialogue systems             | Benchmark conversational AI understanding of context and intent |
+
+---
+
+## Example
+
+```json
+{
+  "id": "5-510",
+  "dialogue_turns": [
+    {
+      "speaker": "M",
+      "text": "I am considering dropping my dancing class. I am not making any progress."
+    },
+    {
+      "speaker": "W",
+      "text": "If I were you, I stick with it. It's definitely worth time and effort."
+    }
+  ],
+  "questions": [
+    {
+      "question_text": "What does the man suggest the woman do?",
+      "answer_text": "Continue her dancing class.",
+      "choices": [
+        "Consult her dancing teacher.",
+        "Take a more interesting class.",
+        "Continue her dancing class."
+      ],
+      "correct_choice_index": 2
+    }
+  ]
+}
+```
+
+## Dataset Statistics
+
+- **Total examples**: 6,444 dialogue-question pairs
+- **Average choices per question**: 3 (standard multiple-choice format)
+- **Source**: Original DREAM dataset
+- **Language**: English
+- **Domain**: General conversational scenarios
+
+## Data Splits
+
+The dataset includes the following splits from the original DREAM dataset:
+
+- Train: ~4,000 examples
+- Dev: ~1,300 examples
+- Test: ~1,300 examples
+
+## Changelog
+
+v1.0.0 · Initial release – transformed original DREAM dataset to Conversation Fact Benchmark format with structured dialogue turns and multiple-choice questions
+
+## Dataset Creation
+
+This dataset was created by transforming the original DREAM dataset into a format suitable for the [Conversation Fact Benchmark](https://github.com/savourylie/Conversation-Fact-Benchmark) framework. The transformation process:
+
+1. Converted raw dialogue text into structured speaker turns
+2. Preserved original multiple-choice questions and answers
+3. Added explicit choice indexing for evaluation
+4. Maintained dialogue context and question associations
+
+## Citation
+
+If you use this dataset, please cite both the original DREAM paper and the Conversation Fact Benchmark:
+
+```bibtex
+@inproceedings{sun2019dream,
+  title={DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension},
+  author={Sun, Kai and Yu, Dian and Chen, Jianshu and Yu, Dong and Choi, Yejin and Cardie, Claire},
+  booktitle={Transactions of the Association for Computational Linguistics},
+  year={2019}
+}
+```
+
+## Contributing
+
+We welcome contributions for:
+
+- Additional data formats (CSV, Parquet)
+- Evaluation scripts and baselines
+- Error analysis and dataset improvements
+
+Please maintain the MIT license and cite appropriately.
+
+## License
+
+This dataset is released under the MIT License, following the original DREAM dataset licensing terms.
+
+Enjoy benchmarking your conversational reading comprehension models!
+# Last updated: Mon Jun 30 16:27:51 HKT 2025

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