# User Engagement Prediction for Clarification in Search

arXiv: https://arxiv.org/abs/2102.04163 · HTML: https://mlsanity.com/paper/2102.04163/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2021.

## Built on by (most cited first, 12 of 12)

- [Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions](https://mlsanity.com/paper/2109.05794/) (2021)
- [Evaluating Mixed-initiative Conversational Search Systems via User Simulation](https://mlsanity.com/paper/2204.08046/) (2022)
- [A Survey of Conversational Search](https://mlsanity.com/paper/2410.15576/) (2024)
- [Analysing Mixed Initiatives and Search Strategies during Conversational Search](https://mlsanity.com/paper/2109.05955/) (2021)
- [Database Search Results Disambiguation for Task-Oriented Dialog Systems](https://mlsanity.com/paper/2112.08351/) (2021)
- [Asking Multimodal Clarifying Questions in Mixed-Initiative Conversational Search](https://mlsanity.com/paper/2402.07742/) (2024)
- [AGENT-CQ: Automatic Generation and Evaluation of Clarifying Questions for Conversational Search with LLMs](https://mlsanity.com/paper/2410.19692/) (2024)
- [A Transformer-Based User Satisfaction Prediction for Proactive Interaction Mechanism in DuerOS](https://mlsanity.com/paper/2212.03817/) (2022)
- [Keyword Extraction for Improved Document Retrieval in Conversational Search](https://mlsanity.com/paper/2109.05979/) (2021)
- [Leveraging Interesting Facts to Enhance User Engagement with Conversational Interfaces](https://mlsanity.com/paper/2404.06659/) (2024)
- [Multi-stage Clarification in Conversational AI: The case of Question-Answering Dialogue Systems](https://mlsanity.com/paper/2110.15235/) (2021)
- [The Emotional Spectrum of LLMs: Leveraging Empathy and Emotion-Based Markers for Mental Health Support](https://mlsanity.com/paper/2412.20068/) (2024)

## Abstract (arXiv)

Clarification is increasingly becoming a vital factor in various topics of information retrieval, such as conversational search and modern Web search engines. Prompting the user for clarification in a search session can be very beneficial to the system as the user's explicit feedback helps the system improve retrieval massively. However, it comes with a very high risk of frustrating the user in case the system fails in asking decent clarifying questions. Therefore, it is of great importance to determine when and how to ask for clarification. To this aim, in this work, we model search clarification prediction as user engagement problem. We assume that the better a clarification is, the higher user engagement with it would be. We propose a Transformer-based model to tackle the task. The comparison with competitive baselines on large-scale real-life clarification engagement data proves the effectiveness of our model. Also, we analyse the effect of all result page elements on the performance and find that, among others, the ranked list of the search engine leads to considerable improvements. Our extensive analysis of task-specific features guides future research.