Recent developments in Information Retrieval and Natural Language Processing are rapidly transforming how users interact with search engines. A more interactive and sophisticated approach supplants the longstanding tradition of sending single, standalone text queries and receiving an immediate response. Users are no longer limited to isolated searches; they participate in multi-turn conversations that mimic dialogues with the search engine.
This shift involves questions, responses, and follow-up queries, resulting in a vibrant Conversational Search (CS) experience. This change underscores technology’s increasing capacity to comprehend and engage with user intent within a contextual and conversational framework. Such a paradigm shift, distinguished by its interactive features, is well-positioned to experience substantial improvements by integrating novel Query Performance Prediction (QPP) techniques, revolving around the prediction of search results or system response quality.
While the potential for leveraging QPP in the CS domain is substantial, this area is relatively nascent and needs a clear framework for its exploration. This study, presented at the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval held in Taipei in July 2023, seeks to bridge this gap by introducing a comprehensive framework for exploiting QPP techniques in the context of Conversational Search. One of the core challenges in the CS domain is defining what it means to predict performance accurately, considering that user information needs manifest as a sequence of closely related utterances rather than independent queries. To address this, we identify in the paper three primary avenues for integrating QPP models in the CS landscape:
- Diagnostic Tool: QPP models can be diagnostic tools to assess and understand performance trends during conversations.
- Behavioral Adjustment: They can be employed to tune and adapt the system’s behavior in real-time as conversations progress.
- Performance Prediction: These models can also forecast the system’s performance on the next user utterance.
In light of the absence of established evaluation procedures for QPP in the CS domain, we introduce a standardized protocol for evaluating QPPs within these usage scenarios.
Additionally, the study presents a collection of spatial-based QPP models designed specifically for the conversational search environment. Given the prevalence of dense neural retrieval models and the typically small query cutoffs in this domain, these models are tailored to perform optimally in such conditions.
Empirical results demonstrate that the proposed QPP approaches yield substantial improvements in predictive performance over existing state-of-the-art methods across various scenarios and collections. This research offers valuable insights into the evolving landscape of Conversational Search and the critical role that QPP techniques can play in enhancing user experiences. Due to its potential impact, the paper was awarded the ACM SIGIR 2023 Best Paper Honorable Mention.
Article originally posted on Medium.
