# Wizard of Search Engine: Access to Information Through Conversations with Search Engines arXiv: https://arxiv.org/abs/2105.08301 ยท HTML: https://mlsanity.com/paper/2105.08301/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 29 of its 51 references that are in the map. - First posted on arXiv 18 May 2021. ## Builds on (most cited first, 29 of 29) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016) - [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016) - [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017) - [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015) - [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018) - [The NarrativeQA Reading Comprehension Challenge](https://mlsanity.com/paper/1712.07040/) (2017) - [Wizard of Wikipedia: Knowledge-Powered Conversational agents](https://mlsanity.com/paper/1811.01241/) (2018) - [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018) - [Asking Clarifying Questions in Open-Domain Information-Seeking Conversations](https://mlsanity.com/paper/1907.06554/) (2019) - [Advances and Challenges in Conversational Recommender Systems: A Survey](https://mlsanity.com/paper/2101.09459/) (2021) - [Conversational Recommender System](https://mlsanity.com/paper/1806.03277/) (2018) - [DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications](https://mlsanity.com/paper/1711.05073/) (2017) - [Temporal Relational Ranking for Stock Prediction](https://mlsanity.com/paper/1809.09441/) (2018) - [Proactive Human-Machine Conversation with Explicit Conversation Goals](https://mlsanity.com/paper/1906.05572/) (2019) - [Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph](https://mlsanity.com/paper/1801.10314/) (2018) - [TREC CAsT 2019: The Conversational Assistance Track Overview](https://mlsanity.com/paper/2003.13624/) (2020) - [KdConv: A Chinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation](https://mlsanity.com/paper/2004.04100/) (2020) - [Towards Exploiting Background Knowledge for Building Conversation Systems](https://mlsanity.com/paper/1809.08205/) (2018) - [Query Resolution for Conversational Search with Limited Supervision](https://mlsanity.com/paper/2005.11723/) (2020) - [Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning](https://mlsanity.com/paper/1704.03084/) (2017) - [Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion](https://mlsanity.com/paper/1910.03262/) (2019) - [Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering](https://mlsanity.com/paper/1607.06275/) (2016) - [User Intent Prediction in Information-seeking Conversations](https://mlsanity.com/paper/1901.03489/) (2019) - [Fluent Response Generation for Conversational Question Answering](https://mlsanity.com/paper/2005.10464/) (2020) - [Thinking Globally, Acting Locally: Distantly Supervised Global-to-Local Knowledge Selection for Background Based Conversation](https://mlsanity.com/paper/1908.09528/) (2019) - [Macaw: An Extensible Conversational Information Seeking Platform](https://mlsanity.com/paper/1912.08904/) (2019) - [Retrospective and Prospective Mixture-of-Generators for Task-oriented Dialogue Response Generation](https://mlsanity.com/paper/1911.08151/) (2019) - [Conversational Search -- A Report from Dagstuhl Seminar 19461](https://mlsanity.com/paper/2005.08658/) (2020) ## Built on by (most cited first, 11 of 11) - [Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies](https://mlsanity.com/paper/2309.13063/) (2023) - [A Survey of Conversational Search](https://mlsanity.com/paper/2410.15576/) (2024) - [Transformer Uncertainty Estimation with Hierarchical Stochastic Attention](https://mlsanity.com/paper/2112.13776/) (2021) - [ReMeDi: Resources for Multi-domain, Multi-service, Medical Dialogues](https://mlsanity.com/paper/2109.00430/) (2021) - [User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systems](https://mlsanity.com/paper/2202.02912/) (2022) - [Metaphorical User Simulators for Evaluating Task-oriented Dialogue Systems](https://mlsanity.com/paper/2204.00763/) (2022) - [Learning to Ask: Conversational Product Search via Representation Learning](https://mlsanity.com/paper/2411.14466/) (2024) - [Engineering Conversational Search Systems: A Review of Applications, Architectures, and Functional Components](https://mlsanity.com/paper/2407.00997/) (2024) - [PSCon: Product Search Through Conversations](https://mlsanity.com/paper/2502.13881/) (2025) - [Learning to Ask Conversational Questions by Optimizing Levenshtein Distance](https://mlsanity.com/paper/2106.15903/) (2021) - [Policy-driven Knowledge Selection and Response Generation for Document-grounded Dialogue](https://mlsanity.com/paper/2410.15970/) (2024) ## Abstract (arXiv) Conversational information seeking (CIS) is playing an increasingly important role in connecting people to information. Due to the lack of suitable resource, previous studies on CIS are limited to the study of theoretical/conceptual frameworks, laboratory-based user studies, or a particular aspect of CIS (e.g., asking clarifying questions). In this work, we make efforts to facilitate research on CIS from three aspects. (1) We formulate a pipeline for CIS with six sub-tasks: intent detection (ID), keyphrase extraction (KE), action prediction (AP), query selection (QS), passage selection (PS), and response generation (RG). (2) We release a benchmark dataset, called wizard of search engine (WISE), which allows for comprehensive and in-depth research on all aspects of CIS. (3) We design a neural architecture capable of training and evaluating both jointly and separately on the six sub-tasks, and devise a pre-train/fine-tune learning scheme, that can reduce the requirements of WISE in scale by making full use of available data. We report some useful characteristics of CIS based on statistics of WISE. We also show that our best performing model variant isable to achieve effective CIS as indicated by several metrics. We release the dataset, the code, as well as the evaluation scripts to facilitate future research by measuring further improvements in this important research direction.