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.

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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)

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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

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Example

{
  "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 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:

@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