mlsanityData to 14 Oct 2026

Leveraging Interesting Facts to Enhance User Engagement with Conversational Interfaces

arXiv 2404.06659 ↗First posted 9 Apr 2024cs.CLcited by 1

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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.

Key facts. Cited by 1 paper 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

11 of 31 references.

References · 11

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  3. 2020Recent Advances and Challenges in Task-oriented Dialog Systemcited by 78
  4. 2019What Makes a Good Conversation? Challenges in Designing Truly Conversational Agentscited by 42
  5. 2022Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systemscited by 15
  6. 2021User Engagement Prediction for Clarification in Searchcited by 12
  7. 2022Task2Dial: A Novel Task and Dataset for Commonsense enhanced Task-based Dialogue Grounded in Documentscited by 7
  8. 2021Ranking Clarifying Questions Based on Predicted User Engagementcited by 6
  9. 2020Large-scale Hybrid Approach for Predicting User Satisfaction with Conversational Agentscited by 4
  10. 2021Can a Humorous Conversational Agent Enhance Learning Experience and Outcomes?cited by 4
  11. 2023The Wizard of Curiosities: Enriching Dialogues with Fun Factscited by 2

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