Modeling User Search Tasks with a Language-Agnostic Unsupervised Approach

Conversational information seeking is a major emerging research area because of the increasing popularity of conversational AI systems users utilize to perform their search tasks. Search systems and multiple other user supporting applications benefit from modeling the search tasks users carry out to satisfy their information needs. Most existing search task modeling methods are monolingual, and few methods leverage user clicks even though clicked URLs are crucial for modeling user intent. We propose a language-agnostic, user intent aware approach to model search tasks from user interactions with search systems. The proposed approach leverages user intent modeling from clicked query-document pairs, latent representations of queries in a language-agnostic space, and graph-based clusteringto model search tasks in an unsupervised approach. Experimental results demonstrate the proposed approach outperforms recent work in search task modeling, supporting user queries in multiple languages. It can also produce search task modeling results in the order of milliseconds, an essential aspect for conversational systems and user support applications requiring realtime results.