Generating Multi-Aspect Queries for Conversational Search.
| Authors | Zahra Abbasiantaeb et al. |
| Year | 2026 |
| Venue | EACL 2026 |
| Paper | View on ACL Anthology |
| PDF | Download |
Abstract
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Engineering Breakdown
Plain English
This paper addresses multi-turn conversational search by generating queries that explore multiple aspects of a user's information need in a single interaction. The work develops methods to automatically generate diverse, aspect-aware queries that improve search effectiveness in dialogue contexts, moving beyond single-turn query reformulation.
Key Engineering Insight
The core insight is that conversational search systems need to proactively generate queries covering different facets of user intent rather than passively responding to explicit reformulations—this requires modeling implicit query aspects and predicting user information needs across conversation context.
Why It Matters for Engineers
Production search and RAG systems struggle with multi-faceted user queries in dialogue. This research directly addresses the problem of generating diverse search candidates automatically, which reduces manual query tuning and improves retrieval coverage for complex information needs—common pain points in chatbot and assistant systems.
Research Context
Prior conversational search work focused on rewriting single queries or handling clarifications. This paper advances the field by introducing aspect-aware generation during conversation, enabling systems to explore multiple dimensions of a search problem without explicit user prompting. This bridges the gap between single-turn retrieval and natural dialogue-based exploration.
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