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Conversational information seeking (CIS) is playing an increasingly importantrole in connecting people to information. Due to the lack of suitable resource,previous studies on CIS are limited to the study of theoretical/conceptualframeworks, laboratory-based user studies, or a particular aspect of CIS (e.g.,asking clarifying questions). In this work, we make efforts to facilitateresearch on CIS from three aspects. (1) We formulate a pipeline for CIS withsix sub-tasks: intent detection (ID), keyphrase extraction (KE), actionprediction (AP), query selection (QS), passage selection (PS), and responsegeneration (RG). (2) We release a benchmark dataset, called wizard of searchengine (WISE), which allows for comprehensive and in-depth research on allaspects of CIS. (3) We design a neural architecture capable of training andevaluating both jointly and separately on the six sub-tasks, and devise apre-train/fine-tune learning scheme, that can reduce the requirements of WISEin scale by making full use of available data. We report some usefulcharacteristics of CIS based on statistics of WISE. We also show that our bestperforming model variant isable to achieve effective CIS as indicated byseveral metrics. We release the dataset, the code, as well as the evaluationscripts to facilitate future research by measuring further improvements in thisimportant research direction.

Wizard of Search Engine: Access to Information Through Conversations with Search Engines