Dialogue-Based Information Retrieval

Kaushik, Abhishek

doi:10.1007/978-3-030-15719-7_50

Cited by 4 publications

(10 citation statements)

References 6 publications

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“…It is difficult to obtain relevant results without refining the search several times, and the results also have to be verified as relevant by the user. Traditional or single-shot searches may result in increased cognitive load and frustration for the user throughout the search procedure, which might lead the searcher to abandon the search process without receiving required information [1,10,11]. We want to investigate the user experience of the interactive conversational search process in this study, which was implemented utilizing the proposed interface presented in the later portion of this article.…”

Section: Motivationmentioning

confidence: 99%

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An Exploratory Study on a Reinforcement Learning Prototype for Multimodal Image Retrieval Using a Conversational Search Interface

2022

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In the realm of information, conversational search is a relatively new trend. In this study, we have developed, implemented, and evaluated a multiview conversational image search system to investigate user search behaviour. We have also explored the potential for reinforcement learning to learn from user search behaviour and support the user in the complex information seeking process. A conversational image search system may mimic a natural language discussion with a user via text or speech, and then assist the user in locating the required picture via a dialogue-based search. We modified and improved a dual-view search interface that displays discussions on one side and photos on the other. Based on the states, incentives, and dialogues in the initial run, we developed a reinforcement learning model and a customized search algorithm in the back end that predicts which reply and images would be provided to the user among a restricted set of fixed responses. Usability of the system was validated using methodologies such as Chatbot Usability Questionnaire, System Usability Scale, and User Experience Questionnaire, and the values were tabulated. The result of this usability experiment proved that most of the users found the system to be very usable and helpful for their image search.

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Section: Motivationmentioning

confidence: 99%

“…Another challenge is the user's shortage of knowledge of the topic about which they are querying. For efficient image retrieval to take place, the user should be able to fully describe what they want to know [1]. To overcome the above-mentioned challenges, an alternative model for search interaction is gaining momentum.…”

Section: Introductionmentioning

confidence: 99%

An Exploratory Study on a Reinforcement Learning Prototype for Multimodal Image Retrieval Using a Conversational Search Interface

2022

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“…There are many limitations associated with a single query search to affect its search success. The following points are the constraints of single-query search, which are intended for application of an interactive search bot (Kaushik, 2019;Kaushik et al, 2020a):…”

Section: 1mentioning

confidence: 99%

Communication is the Universal Solvent: Usability Study on Atreya Bot – An Interactive Bot for Chemical Scientists

Mahak¹,

Kaushik

Shubham

et al. 2022

SSRN Journal

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“…1. Cognitive Load: Conventional search can impose a significant cognitive load on the searcher [17]. An important factor in the evaluation of conversational systems is measurement of the cognitive load experienced by users while using the system.…”

Section: Essential Factors For Conversational Searchmentioning

confidence: 99%

“…In these studies, we have examined user behaviour and user expectations with respect to CS. Based on our investigations [2,[17][18][19][20], we propose an evaluation framework for CS interfaces. The framework is divided into two segments: Exploration and Contentment, as shown in Figure 1.…”

Section: Designing Our Conceptual Frameworkmentioning

confidence: 99%

A Conceptual Framework for Implicit Evaluation of Conversational Search Interfaces

Kaushik,

Jones

2021

Preprint

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Conversational search (CS) has recently become a significant focus of the information retrieval (IR) research community. Multiple studies have been conducted which explore the concept of conversational search. Understanding and advancing research in CS requires careful and detailed evaluation. Existing CS studies have been limited to evaluation based on simple user feedback on task completion. We propose a CS evaluation framework which includes multiple dimensions: search experience, knowledge gain, software usability, cognitive load and user experience, based on studies of conversational systems and IR. We introduce these evaluation criteria and propose their use in a framework for the evaluation of CS systems.

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Dialogue-Based Information Retrieval

Cited by 4 publications

References 6 publications

An Exploratory Study on a Reinforcement Learning Prototype for Multimodal Image Retrieval Using a Conversational Search Interface

An Exploratory Study on a Reinforcement Learning Prototype for Multimodal Image Retrieval Using a Conversational Search Interface

Communication is the Universal Solvent: Usability Study on Atreya Bot – An Interactive Bot for Chemical Scientists

A Conceptual Framework for Implicit Evaluation of Conversational Search Interfaces

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