# Leveraging Interesting Facts to Enhance User Engagement with Conversational Interfaces

arXiv: https://arxiv.org/abs/2404.06659 · HTML: https://mlsanity.com/paper/2404.06659/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 9 Apr 2024.

## Builds on (most cited first, 11 of 11)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Recent Advances and Challenges in Task-oriented Dialog System](https://mlsanity.com/paper/2003.07490/) (2020)
- [What Makes a Good Conversation? Challenges in Designing Truly Conversational Agents](https://mlsanity.com/paper/1901.06525/) (2019)
- [Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems](https://mlsanity.com/paper/2206.07808/) (2022)
- [User Engagement Prediction for Clarification in Search](https://mlsanity.com/paper/2102.04163/) (2021)
- [Task2Dial: A Novel Task and Dataset for Commonsense enhanced Task-based Dialogue Grounded in Documents](https://mlsanity.com/paper/2204.01061/) (2022)
- [Ranking Clarifying Questions Based on Predicted User Engagement](https://mlsanity.com/paper/2103.06192/) (2021)
- [Large-scale Hybrid Approach for Predicting User Satisfaction with Conversational Agents](https://mlsanity.com/paper/2006.07113/) (2020)
- [Can a Humorous Conversational Agent Enhance Learning Experience and Outcomes?](https://mlsanity.com/paper/2108.11259/) (2021)
- [The Wizard of Curiosities: Enriching Dialogues with Fun Facts](https://mlsanity.com/paper/2309.11283/) (2023)

## Built on by (most cited first, 1 of 1)

- [Large-Scale User Behavior Analysis in Multimodal AI-Assisted Manual Task Execution](https://mlsanity.com/paper/2609.07594/) (2026)

## Abstract (arXiv)

Conversational Task Assistants (CTAs) guide users in performing a multitude of activities, such as making recipes. However, ensuring that interactions remain engaging, interesting, and enjoyable for CTA users is not trivial, especially for time-consuming or challenging tasks. Grounded in psychological theories of human interest, we propose to engage users with contextual and interesting statements or facts during interactions with a multi-modal CTA, to reduce fatigue and task abandonment before a task is complete. To operationalize this idea, we train a high-performing classifier (82% F1-score) to automatically identify relevant and interesting facts for users. We use it to create an annotated dataset of task-specific interesting facts for the domain of cooking. Finally, we design and validate a dialogue policy to incorporate the identified relevant and interesting facts into a conversation, to improve user engagement and task completion. Live testing on a leading multi-modal voice assistant shows that 66% of the presented facts were received positively, leading to a 40% gain in the user satisfaction rating, and a 37% increase in conversation length. These findings emphasize that strategically incorporating interesting facts into the CTA experience can promote real-world user participation for guided task interactions.