Supporting Complex Information-Seeking Tasks with Implicit Constraints

Current interactive systems with natural language interface lack an ability to understand a complex information-seeking request which expresses several implicit constraints at once, and there is no prior information about user preferences, e.g., "find hiking trails around San Francisco which are accessible with toddlers and have beautiful scenery in summer", where output is a list of possible suggestions for users to start their exploration. In such scenarios, the user requests can be issued at once in the form of a complex and long query, unlike conversational and exploratory search models that require short utterances or queries where they often require to be fed into the system step by step. This advancement provides the final user more flexibility and precision in expressing their intent through the search process. Such systems are inherently helpful for day-today user tasks requiring planning that are usually time-consuming, sometimes tricky, and cognitively taxing. We have designed and deployed a platform to collect the data from approaching such complex interactive systems. In this paper, we propose an Interactive Agent (IA) that allows intricately refined user requests by making it complete, which should lead to better retrieval. To demonstrate the performance of the proposed modeling paradigm, we have adopted various pre-retrieval metrics that capture the extent to which guided interactions with our system yield better retrieval results. Through extensive experimentation, we demonstrated that our method significantly outperforms several robust baselines