Conversational information seeking (CIS) is concerned with a sequence of interactions between one or more users and an information system. Interactions in CIS are primarily based on natural language dialogue, while they may include other types of interactions, such as click, touch, and body gestures. This monograph provides a thorough overview of CIS definitions, applications, interactions, interfaces, design, implementation, and evaluation. This monograph views CIS applications as including conversational search, conversational question answering, and conversational recommendation. Our aim is to provide an overview of past research related to CIS, introduce the current state-of-the-art in CIS, highlight the challenges still being faced in the community. and suggest future directions.
Conversational information seeking (CIS) has been recognized as a major emerging research area in information retrieval. Such research will require data and tools, to allow the implementation and study of conversational systems. This paper introduces Macaw, an open-source framework with a modular architecture for CIS research. Macaw supports multi-turn, multi-modal, and mixed-initiative interactions, and enables research for tasks such as document retrieval, question answering, recommendation, and structured data exploration. It has a modular design to encourage the study of new CIS algorithms…
Conversational search is an approach to information retrieval (IR), where users engage in a dialogue with an agent in order to satisfy their information needs. Previous conceptual work described properties and actions a good agent should exhibit. Unlike them, we present a novel conceptual model defined in terms of conversational goals, which enables us to reason about current research practices in conversational search. Based on the literature, we elicit how existing tasks and test collections from the fields of IR, natural language processing (NLP) and dialogue systems (DS) fit into this mode…
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；RMIT University（皇家墨尔本理工大学）；Rutgers University（罗格斯大学）；Alibaba Group（阿里巴巴集团）
Understanding and characterizing how people interact in information-seeking conversations is crucial in developing conversational search systems. In this paper, we introduce a new dataset designed for this purpose and use it to analyze information-seeking conversations by user intent distribution, co-occurrence, and flow patterns. The MSDialog dataset is a labeled dialog dataset of question answering (QA) interactions between information seekers and providers from an online forum on Microsoft products. The dataset contains more than 2,000 multi-turn QA dialogs with 10,000 utterances that are a…
University of Glasgow（格拉斯哥大学）；Microsoft Research（微软研究院）；Carnegie Mellon University（卡内基梅隆大学）
The Conversational Assistance Track (CAsT) is a new track for TREC 2019 to facilitate Conversational Information Seeking (CIS) research and to create a large-scale reusable test collection for conversational search systems. The document corpus is 38,426,252 passages from the TREC Complex Answer Retrieval (CAR) and Microsoft MAchine Reading COmprehension (MARCO) datasets. Eighty information seeking dialogues (30 train, 50 test) are an average of 9 to 10 questions long. Relevance assessments are provided for 30 training topics and 20 test topics. This year 21 groups submitted a total of 65 runs …
Technical University of Munich（慕尼黑工业大学）；University of Twente（特温特大学）
Conversational search has evolved as a new information retrieval paradigm, marking a shift from traditional search systems towards interactive dialogues with intelligent search agents. This change especially affects exploratory information-seeking contexts, where conversational search systems can guide the discovery of unfamiliar domains. In these scenarios, users find it often difficult to express their information goals due to insufficient background knowledge. Conversational interfaces can provide assistance by eliciting information needs and narrowing down the search space. However, due to…
This paper aims to provide a radical rundown on Conversation Search (ConvSearch), an approach to enhance the information retrieval method where users engage in a dialogue for the information-seeking tasks. In this survey, we predominantly focused on the human interactive characteristics of the ConvSearch systems, highlighting the operations of the action modules, likely the Retrieval system, Question-Answering, and Recommender system. We labeled various ConvSearch research problems in knowledge bases, natural language processing, and dialogue management systems along with the action modules. W…
University of Waterloo（滑铁卢大学）；Academia Sinica（中央研究院）；National Chengchi University（国立政治大学）
Conversational search plays a vital role in conversational information seeking. As queries in information seeking dialogues are ambiguous for traditional ad-hoc information retrieval (IR) systems due to the coreference and omission resolution problems inherent in natural language dialogue, resolving these ambiguities is crucial. In this paper, we tackle conversational passage retrieval (ConvPR), an important component of conversational search, by addressing query ambiguities with query reformulation integrated into a multi-stage ad-hoc IR system. Specifically, we propose two conversational que…
Mila（米拉人工智能研究所）；McGill University（麦吉尔大学）；ETH Zürich（苏黎世联邦理工学院）；Microsoft Montréal（微软蒙特利尔）；ServiceNow Research（ServiceNow研究院）；Facebook CIFAR AI Chair（Facebook-CIFAR人工智能讲席）
In a conversational question answering scenario, a questioner seeks to extract information about a topic through a series of interdependent questions and answers. As the conversation progresses, they may switch to related topics, a phenomenon commonly observed in information-seeking search sessions. However, current datasets for conversational question answering are limiting in two ways: 1) they do not contain topic switches; and 2) they assume the reference text for the conversation is given, i.e., the setting is not open-domain. We introduce TopiOCQA (pronounced Tapioca), an open-domain conv…
AI Institute, University of South Carolina（南卡罗来纳大学人工智能研究所）；Samsung Research America（三星美国研究院）
Conversational Information Seeking (CIS) is a relatively new research area within conversational AI that attempts to seek information from end-users in order to understand and satisfy users' needs. If realized, such a system has far-reaching benefits in the real world; for example, a CIS system can assist clinicians in pre-screening or triaging patients in healthcare. A key open sub-problem in CIS that remains unaddressed in the literature is generating Information Seeking Questions (ISQs) based on a short initial query from the end-user. To address this open problem, we propose Information SE…
Università della Svizzera italiana (USI)（瑞士意大利语区大学（USI））；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
Users often fail to formulate their complex information needs in a single query. As a consequence, they may need to scan multiple result pages or reformulate their queries, which may be a frustrating experience. Alternatively, systems can improve user satisfaction by proactively asking questions of the users to clarify their information needs. Asking clarifying questions is especially important in conversational systems since they can only return a limited number of (often only one) result(s). In this paper, we formulate the task of asking clarifying questions in open-domain information-seekin…
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Huawei Poisson Lab（华为泊松实验室）；Waseda University（早稻田大学）
Conversational interfaces that allow for intuitive and comprehensive access to digitally stored information remain an ambitious goal. In this thesis, we lay foundations for designing conversational search systems by analyzing the requirements and proposing concrete solutions for automating some of the basic components and tasks that such systems should support. We describe several interdependent studies that were conducted to analyse the design requirements for more advanced conversational search systems able to support complex human-like dialogue interactions and provide access to vast knowle…
Conversational question answering aims to provide natural-language answers to users in information-seeking conversations. Existing conversational QA benchmarks compare models with pre-collected human-human conversations, using ground-truth answers provided in conversational history. It remains unclear whether we can rely on this static evaluation for model development and whether current systems can well generalize to real-world human-machine conversations. In this work, we conduct the first large-scale human evaluation of state-of-the-art conversational QA systems, where human evaluators conv…
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Ant Financial Services Group（蚂蚁集团）
Recent studies on Question Answering (QA) and Conversational QA (ConvQA) emphasize the role of retrieval: a system first retrieves evidence from a large collection and then extracts answers. This open-retrieval ConvQA setting typically assumes that each question is answerable by a single span of text within a particular passage (a span answer). The supervision signal is thus derived from whether or not the system can recover an exact match of this ground-truth answer span from the retrieved passages. This method is referred to as span-match weak supervision. However, information-seeking conver…
A conversational information retrieval (CIR) system is an information retrieval (IR) system with a conversational interface which allows users to interact with the system to seek information via multi-turn conversations of natural language, in spoken or written form. Recent progress in deep learning has brought tremendous improvements in natural language processing (NLP) and conversational AI, leading to a plethora of commercial conversational services that allow naturally spoken and typed interaction, increasing the need for more human-centric interactions in IR. As a result, we have witnesse…
Conversational search is a relatively young area of research that aims at automating an information-seeking dialogue. In this paper we help to position it with respect to other research areas within conversational Artificial Intelligence (AI) by analysing the structural properties of an information-seeking dialogue. To this end, we perform a large-scale dialogue analysis of more than 150K transcripts from 16 publicly available dialogue datasets. These datasets were collected to inform different dialogue-based tasks including conversational search. We extract different patterns of mixed initiat…
The rise of intelligent assistant systems like Siri and Alexa have led to the emergence of Conversational Search, a research track of Information Retrieval (IR) that involves interactive and iterative information-seeking user-system dialog. Recently released OR-QuAC and TCAsT19 datasets narrow their research focus on the retrieval aspect of conversational search i.e. fetching the relevant documents (passages) from a large collection using the conversational search history. Currently proposed models for these datasets incorporate history in retrieval by appending the last N turns to the current…
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Rutgers University（罗格斯大学）；RMIT University（皇家墨尔本理工大学）；Alibaba Group（阿里巴巴集团）
Conversational assistants are being progressively adopted by the general population. However, they are not capable of handling complicated information-seeking tasks that involve multiple turns of information exchange. Due to the limited communication bandwidth in conversational search, it is important for conversational assistants to accurately detect and predict user intent in information-seeking conversations. In this paper, we investigate two aspects of user intent prediction in an information-seeking setting. First, we extract features based on the content, structural, and sentiment charac…
In this work, we analyze how human gaze during reading comprehension is conditioned on the given reading comprehension question, and whether this signal can be beneficial for machine reading comprehension. To this end, we collect a new eye-tracking dataset with a large number of participants engaging in a multiple choice reading comprehension task. Our analysis of this data reveals increased fixation times over parts of the text that are most relevant for answering the question. Motivated by this finding, we propose making automated reading comprehension more human-like by mimicking human info…
How can we better understand the mechanisms behind multi-turn information seeking dialogues? How can we use these insights to design a dialogue system that does not require explicit query formulation upfront as in question answering? To answer these questions, we collected observations of human participants performing a similar task to obtain inspiration for the system design. Then, we studied the structure of conversations that occurred in these settings and used the resulting insights to develop a grounded theory, design and evaluate a first system prototype. Evaluation results show that our…
Vienna University of Economics and Business（维也纳经济大学）；Federal University of Rio de Janeiro（里约热内卢联邦大学）；University of Amsterdam（阿姆斯特丹大学）
Understanding the structure of interaction processes helps us to improve information-seeking dialogue systems. Analyzing an interaction process boils down to discovering patterns in sequences of alternating utterances exchanged between a user and an agent. Process mining techniques have been successfully applied to analyze structured event logs, discovering the underlying process models or evaluating whether the observed behavior is in conformance with the known process. In this paper, we apply process mining techniques to discover patterns in conversational transcripts and extract a new model…
Conversation is the natural mode for information exchange in daily life, a spoken conversational interaction for search input and output is a logical format for information seeking. However, the conceptualisation of user-system interactions or information exchange in spoken conversational search (SCS) has not been explored. The first step in conceptualising SCS is to understand the conversational moves used in an audio-only communication channel for search. This paper explores conversational actions for the task of search. We define a qualitative methodology for creating conversational dataset…
Iowa State University（爱荷华州立大学）；Microsoft Research（微软研究院）
Existing studies in conversational AI mostly treat task-oriented dialog (TOD) and question answering (QA) as separate tasks. Towards the goal of constructing a conversational agent that can complete user tasks and support information seeking, it is important to build a system that handles both TOD and QA with access to various external knowledge. In this work, we propose a new task, Open-Book TOD (OB-TOD), which combines TOD with QA task and expand external knowledge sources to include both explicit knowledge sources (e.g., the Web) and implicit knowledge sources (e.g., pre-trained language mo…
The ability to engage in mixed-initiative interaction is one of the core requirements for a conversational search system. How to achieve this is poorly understood. We propose a set of unsupervised metrics, termed ConversationShape, that highlights the role each of the conversation participants plays by comparing the distribution of vocabulary and utterance types. Using ConversationShape as a lens, we take a closer look at several conversational search datasets and compare them with other dialogue datasets to better understand the types of dialogue interaction they represent, either driven by t…
Center for Intelligent Information Retrieval, University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校智能信息检索中心）；Alibaba Group（阿里巴巴集团）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Dept. of Computer Science, Rutgers University（罗格斯大学计算机科学系）
Intelligent personal assistant systems with either text-based or voice-based conversational interfaces are becoming increasingly popular around the world. Retrieval-based conversation models have the advantages of returning fluent and informative responses. Most existing studies in this area are on open domain "chit-chat" conversations or task / transaction oriented conversations. More research is needed for information-seeking conversations. There is also a lack of modeling external knowledge beyond the dialog utterances among current conversational models. In this paper, we propose a learnin…
Conversational search is based on a user-system cooperation with the objective to solve an information-seeking task. In this report, we discuss the implication of such cooperation with the learning perspective from both user and system side. We also focus on the stimulation of learning through a key component of conversational search, namely the multimodality of communication way, and discuss the implication in terms of information retrieval. We end with a research road map describing promising research directions and perspectives.
Understanding User Preferences for Interaction Styles in Conversational Recommender Systems: The Predictive Role of System Qualities, User Experience, and Traits
School of Computer Science, University of Technology Sydney（技术科技大学计算机科学学院）；Australian Institute of Health Innovation, Macquarie University（麦觉里大学健康创新研究所）
It is natural for humans to collaborate while dealing with complex problems. In this article I consider this process of collaboration in the context of information seeking. The study and discussion presented here are driven by two dissatisfactions: (1) the majority of IR systems today do not facilitate collaboration directly, and (2) the concept of collaboration itself is not well-understood. I begin by probing the notion of collaboration and propose a model that helps us understand the requirements for a successful collaboration. A model of a Collaborative Information Seeking (CIS) environmen…
Evolving Paradigms in Task-Based Search and Learning: A Comparative Analysis of Traditional Search Engine with LLM-Enhanced Conversational Search System
Cognitive Dimensions is a framework for analyzing human-computer interaction. It is used for meta-analysis, that is, for talking about characteristics of systems without getting bogged down in details of a particular implementation. In this paper, I discuss some of the dimensions of this theory and how they can be applied to analyze information seeking interfaces. The goal of this analysis is to introduce a useful vocabulary that practitioners and researchers can use to describe systems, and to guide interface design toward more usable and useful systems
People can help other people find information in networked information seeking environments. Recently, many such systems and algorithms have proliferated in industry and in academia. Unfortunately, it is difficult to compare the systems in meaningful ways because they often define collaboration in different ways. In this paper, we propose a model of possible kinds of collaboration, and illustrate it with examples from literature. The model contains four dimensions: intent, depth, concurrency and location. This model can be used to classify existing systems and to suggest possible opportunities…
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Alibaba Group（阿里巴巴集团）；Ant Financial Services Group（蚂蚁金融服务集团）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Rutgers University（罗格斯大学）
Personal assistant systems, such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana, are becoming ever more widely used. Understanding user intent such as clarification questions, potential answers and user feedback in information-seeking conversations is critical for retrieving good responses. In this paper, we analyze user intent patterns in information-seeking conversations and propose an intent-aware neural response ranking model "IART", which refers to "Intent-Aware Ranking with Transformers". IART is built on top of the integration of user intent modeling and language r…
Despite the many implicit references to the social aspects of search within Information Seeking and Retrieval research, there has been relatively little work that has specifically investigated the additional requirements for collaborative search software. In this paper we re-assess a recent evaluation framework, designed for individual information seeking experiences, to see a) how it could still be applied to collaborative search software; b) how it could produce additional requirements for collaborative search; and c) how it could be extended in future work to be even more appropriate for co…
Information-seeking conversation system aims at satisfying the information needs of users through conversations. Text matching between a user query and a pre-collected question is an important part of the information-seeking conversation in E-commerce. In the practical scenario, a sort of questions always correspond to a same answer. Naturally, these questions can form a bag. Learning the matching between user query and bag directly may improve the conversation performance, denoted as query-bag matching. Inspired by such opinion, we propose a query-bag matching model which mainly utilizes the …
Information seeking process is an important topic in information seeking behavior research. Both qualitative and empirical methods have been adopted in analyzing information seeking processes, with major focus on uncovering the latent search tactics behind user behaviors. Most of the existing works require defining search tactics in advance and coding data manually. Among the few works that can recognize search tactics automatically, they missed making sense of those tactics. In this paper, we proposed using an automatic technique, i.e. the Hidden Markov Model (HMM), to explicitly model the se…
Rather than using (proxies of) end user or expert judgment to decide on the ranking of information, this paper asks whether conversations about information quality might offer a feasible and valuable addition for ranking information. We introduce a theoretical framework for information quality, outlining how information interaction should be perceived as a conversation and quality be evaluated as a conversational contribution. Next, an overview is given of different systems of social alignment and their value for assessing quality and ranking information. We propose that a collaborative approa…
