# User Intent Prediction in Information-seeking Conversations

arXiv: https://arxiv.org/abs/1901.03489 · HTML: https://mlsanity.com/paper/1901.03489/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2019.

## Built on by (most cited first, 19 of 19)

- [Into the Unknown Unknowns: Engaged Human Learning through Participation in Language Model Agent Conversations](https://mlsanity.com/paper/2408.15232/) (2024)
- [Evaluating Mixed-initiative Conversational Search Systems via User Simulation](https://mlsanity.com/paper/2204.08046/) (2022)
- [Self-seeding and Multi-intent Self-instructing LLMs for Generating Intent-aware Information-Seeking dialogs](https://mlsanity.com/paper/2402.11633/) (2024)
- [Cultural Learning-Based Culture Adaptation of Language Models](https://mlsanity.com/paper/2504.02953/) (2025)
- [Wizard of Search Engine: Access to Information Through Conversations with Search Engines](https://mlsanity.com/paper/2105.08301/) (2021)
- [LARA: Linguistic-Adaptive Retrieval-Augmentation for Multi-Turn Intent Classification](https://mlsanity.com/paper/2403.16504/) (2024)
- [Imagining a Future of Designing with AI: Dynamic Grounding, Constructive Negotiation, and Sustainable Motivation](https://mlsanity.com/paper/2402.07342/) (2024)
- [Intent Tagging: Exploring Micro-Prompting Interactions for Supporting Granular Human-GenAI Co-Creation Workflows](https://mlsanity.com/paper/2502.18737/) (2025)
- [Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review](https://mlsanity.com/paper/2308.08496/) (2023)
- [Estimating the Usefulness of Clarifying Questions and Answers for Conversational Search](https://mlsanity.com/paper/2401.11463/) (2024)
- [BioMedSearch: A Multi-Source Biomedical Retrieval Framework Based on LLMs](https://mlsanity.com/paper/2510.13926/) (2025)
- [From Intents to Conversations: Generating Intent-Driven Dialogues with Contrastive Learning for Multi-Turn Classification](https://mlsanity.com/paper/2411.14252/) (2024)
- [Learning functional sections in medical conversations: iterative pseudo-labeling and human-in-the-loop approach](https://mlsanity.com/paper/2210.02658/) (2022)
- [A Survey on Conversational Search and Applications in Biomedicine](https://mlsanity.com/paper/2211.15328/) (2022)
- [A Concept Knowledge Graph for User Next Intent Prediction at Alipay](https://mlsanity.com/paper/2301.00503/) (2023)
- [Intent-calibrated Self-training for Answer Selection in Open-domain Dialogues](https://mlsanity.com/paper/2307.06703/) (2023)
- [Amulet: Putting Complex Multi-Turn Conversations on the Stand with LLM Juries](https://mlsanity.com/paper/2505.20451/) (2025)
- [LUMI: Unsupervised Intent Clustering with Multiple Pseudo-Labels](https://mlsanity.com/paper/2510.14640/) (2025)
- [Structure-Aware RAG: Structured Retrieval Augmented Generation from Noisy Data for Conversational Agents](https://mlsanity.com/paper/2605.24366/) (2026)

## Abstract (arXiv)

Conversational assistants are being progressively adopted by the general population. However, they are not capable of handling complicated information-seeking tasks that involve multiple turns of information exchange. Due to the limited communication bandwidth in conversational search, it is important for conversational assistants to accurately detect and predict user intent in information-seeking conversations. In this paper, we investigate two aspects of user intent prediction in an information-seeking setting. First, we extract features based on the content, structural, and sentiment characteristics of a given utterance, and use classic machine learning methods to perform user intent prediction. We then conduct an in-depth feature importance analysis to identify key features in this prediction task. We find that structural features contribute most to the prediction performance. Given this finding, we construct neural classifiers to incorporate context information and achieve better performance without feature engineering. Our findings can provide insights into the important factors and effective methods of user intent prediction in information-seeking conversations.