Conversational Search: Towards Personalization and Evaluation

Conversational information seeking (CIS) systems aim to understand a user's evolving information needs in the context of a conversation and respond effectively. These systems are especially valuable for users who may struggle with traditional interfaces, offering a more natural and accessible mode of interaction. To ensure the groundedness and accuracy of responses, existing methods break the task into several subtasks, namely, dialogue context modeling, retrieval, and answer generation. The user's information need is often represented by either a single rewritten query or a single representation in the query embedding space. This leads to several limitations, especially in cases where the information need cannot be answered using a single passage and requires complex reasoning over multiple facts from different sources. We address this limitation by proposing the MQ4CS model, which breaks the information need of the user into multiple queries with different aspects. In MQ4CS, the retrieval is done for each query and rank list fusion is done over the list of documents retrieved for each query.