# Interview: A Large-Scale Open-Source Corpus of Media Dialog

arXiv: https://arxiv.org/abs/2004.03090 · HTML: https://mlsanity.com/paper/2004.03090/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization](https://mlsanity.com/paper/2103.06410/) (2021)
- [LiveChat: A Large-Scale Personalized Dialogue Dataset Automatically Constructed from Live Streaming](https://mlsanity.com/paper/2306.08401/) (2023)
- [NewsInterview: a Dataset and a Playground to Evaluate LLMs' Ground Gap via Informational Interviews](https://mlsanity.com/paper/2411.13779/) (2024)
- [My LLM might Mimic AAE -- But When Should it?](https://mlsanity.com/paper/2502.04564/) (2025)
- [Whom to Respond To? A Transformer-Based Model for Multi-Party Social Robot Interaction](https://mlsanity.com/paper/2507.10960/) (2025)

## Abstract (arXiv)

Existing conversational datasets consist either of written proxies for dialog or small-scale transcriptions of natural speech. We introduce 'Interview': a large-scale (105K conversations) media dialog dataset collected from news interview transcripts. Compared to existing large-scale proxies for conversational data, language models trained on our dataset exhibit better zero-shot out-of-domain performance on existing spoken dialog datasets, demonstrating its usefulness in modeling real-world conversations. 'Interview' contains speaker role annotations for each turn, facilitating the development of engaging, responsive dialog systems. In fact, experiments on two dialog tasks show that leveraging such labels improves performance over strong speaker-agnostic baselines, and enabling models to generate more specific and inquisitive responses in interview-style conversations.