README.md

---
# ====== 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