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In this paper, we address the problem of answering complex information needsby conversing conversations with search engines, in the sense that users canexpress their queries in natural language, and directly receivethe informationthey need from a short system response in a conversational manner. Recently,there have been some attempts towards a similar goal, e.g., studies onConversational Agents (CAs) and Conversational Search (CS). However, theyeither do not address complex information needs, or they are limited to thedevelopment of conceptual frameworks and/or laboratory-based user studies.  We pursue two goals in this paper: (1) the creation of a suitable dataset,the Search as a Conversation (SaaC) dataset, for the development of pipelinesfor conversations with search engines, and (2) the development ofastate-of-the-art pipeline for conversations with search engines, theConversations with Search Engines (CaSE), using this dataset. SaaC is builtbased on a multi-turn conversational search dataset, where we further employworkers from a crowdsourcing platform to summarize each relevant passage into ashort, conversational response. CaSE enhances the state-of-the-art byintroducing a supporting token identification module and aprior-aware pointergenerator, which enables us to generate more accurate responses.  We carry out experiments to show that CaSE is able to outperform strongbaselines. We also conduct extensive analyses on the SaaC dataset to show wherethere is room for further improvement beyond CaSE. Finally, we release the SaaCdataset and the code for CaSE and all models used for comparison to facilitatefuture research on this topic.

Conversations with Search Engines: SERP-based Conversational Response Generation