# Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity

arXiv: https://arxiv.org/abs/2005.00172 · HTML: https://mlsanity.com/paper/2005.00172/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2020.

## Built on by (most cited first, 10 of 10)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension](https://mlsanity.com/paper/2107.12708/) (2021)
- [INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactions](https://mlsanity.com/paper/2207.00746/) (2022)
- [ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and Knowledge Graphs](https://mlsanity.com/paper/2112.07622/) (2021)
- [MVP: Multi-task Supervised Pre-training for Natural Language Generation](https://mlsanity.com/paper/2206.12131/) (2022)
- [Response-conditioned Turn-taking Prediction](https://mlsanity.com/paper/2305.02036/) (2023)
- [CESAR: Automatic Induction of Compositional Instructions for Multi-turn Dialogs](https://mlsanity.com/paper/2311.17376/) (2023)
- [DIEKAE: Difference Injection for Efficient Knowledge Augmentation and Editing of Large Language Models](https://mlsanity.com/paper/2406.10660/) (2024)
- [Efficient Intent-Based Filtering for Multi-Party Conversations Using Knowledge Distillation from LLMs](https://mlsanity.com/paper/2503.17336/) (2025)
- [IntrEx: A Dataset for Modeling Engagement in Educational Conversations](https://mlsanity.com/paper/2509.06652/) (2025)

## Abstract (arXiv)

Open-ended human learning and information-seeking are increasingly mediated by digital assistants. However, such systems often ignore the user's pre-existing knowledge. Assuming a correlation between engagement and user responses such as "liking" messages or asking followup questions, we design a Wizard-of-Oz dialog task that tests the hypothesis that engagement increases when users are presented with facts related to what they know. Through crowd-sourcing of this experiment, we collect and release 14K dialogs (181K utterances) where users and assistants converse about geographic topics like geopolitical entities and locations. This dataset is annotated with pre-existing user knowledge, message-level dialog acts, grounding to Wikipedia, and user reactions to messages. Responses using a user's prior knowledge increase engagement. We incorporate this knowledge into a multi-task model that reproduces human assistant policies and improves over a BERT content model by 13 mean reciprocal rank points.