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Journal articles on the topic 'Conversational systems'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Qin, Zhen (Luther). "Conversational Breakdown Detector for a Motivational Interviewing Conversational Agent." IJournal: Student Journal of the Faculty of Information 9, no. 1 (2023): 60–77. http://dx.doi.org/10.33137/ijournal.v9i1.42237.

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A conversational breakdown in human-chatbot interaction refers to a disruption or failure in the communicative flow between the human user and the chatbot. To recover a disrupted conversation, the first step is to detect the breakdown. Researchers have proposed methods using supervised learning and semi-supervised learning in dialogue systems to achieve the goal of detecting conversational breakdown. However, few studies have focused on detecting breakdowns in automated therapeutic conversations, especially conversations led by motivational interviewing chatbots. The presence of conversational

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Watkinson, Neftali, Fedor Zaitsev, Aniket Shivam, et al. "EdgeAvatar: An Edge Computing System for Building Virtual Beings." Electronics 10, no. 3 (2021): 229. http://dx.doi.org/10.3390/electronics10030229.

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Dialogue systems, also known as conversational agents, are computing systems that use algorithms for speech and language processing to engage in conversation with humans or other conversation-capable systems. A chatbot is a conversational agent that has, as its primary goal, to maximize the length of the conversation without any specific targeted task. When a chatbot is embellished with an artistic approach that is meant to evoke an emotional response, then it is called a virtual being. On the other hand, conversational agents that interact with the physical world require the use of specialize

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Shridhar, Marri. "Conceptual Frameworks for Conversational Human-AI Interaction (CHAI) in Professional Contexts." International Journal of Current Science Research and Review 07, no. 10 (2024): 7842–53. https://doi.org/10.5281/zenodo.13943154.

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Abstract : Artificial intelligence (AI) is revolutionizing sectors like financial services, healthcare, and education, driving unprecedented progress and fostering innovation across domains. In this backdrop, basic conversational interface <em>aka </em>chat emerged as the predominant way to interact with AI systems. However, the current Human-AI (H2AI) conversations are fraught with a host of challenges necessitating a critical exploration into their design, strategy, and implications. Human-AI interaction design is hindered by fragmented and disjointed technology-driven approaches t

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Kiesel, Johannes, Lars Meyer, Martin Potthast, and Benno Stein. "Meta-Information in Conversational Search." ACM Transactions on Information Systems 39, no. 4 (2021): 1–44. http://dx.doi.org/10.1145/3468868.

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The exchange of meta-information has always formed part of information behavior. In this article, we show that this rule also extends to conversational search. Information about the user’s information need, their preferences, and the quality of search results are only some of the most salient examples of meta-information that are exchanged as a matter of course in a search conversation. To understand the importance of meta-information for conversational search, we revisit its definition and survey how meta-information has been taken into account in the past in information retrieval. Meta-infor

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Kum, Junyeong, and Myungho Lee. "Can Gestural Filler Reduce User-Perceived Latency in Conversation with Digital Humans?" Applied Sciences 12, no. 21 (2022): 10972. http://dx.doi.org/10.3390/app122110972.

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The demand for a conversational system with digital humans has increased with the development of artificial intelligence. Latency can occur in such conversational systems because of natural language processing and network issues, which can deteriorate the user’s performance and the availability of the systems. There have been attempts to mitigate user-perceived latency by using conversational fillers in human–agent interaction and human–robot interaction. However, non-verbal cues, such as gestures, have received less attention in such attempts, despite their essential roles in communication. T

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Lin, Dongding, Jian Wang, and Wenjie Li. "COLA: Improving Conversational Recommender Systems by Collaborative Augmentation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4462–70. http://dx.doi.org/10.1609/aaai.v37i4.25567.

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Conversational recommender systems (CRS) aim to employ natural language conversations to suggest suitable products to users. Understanding user preferences for prospective items and learning efficient item representations are crucial for CRS. Despite various attempts, earlier studies mostly learned item representations based on individual conversations, ignoring item popularity embodied among all others. Besides, they still need support in efficiently capturing user preferences since the information reflected in a single conversation is limited. Inspired by collaborative filtering, we propose

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Huang, Ting-Hao, Walter Lasecki, Amos Azaria, and Jeffrey Bigham. ""Is There Anything Else I Can Help You With?" Challenges in Deploying an On-Demand Crowd-Powered Conversational Agent." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 4 (September 21, 2016): 79–88. http://dx.doi.org/10.1609/hcomp.v4i1.13292.

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Intelligent conversational assistants, such as Apple's Siri, Microsoft's Cortana, and Amazon's Echo, have quickly become a part of our digital life. However, these assistants have major limitations, which prevents users from conversing with them as they would with human dialog partners. This limits our ability to observe how users really want to interact with the underlying system. To address this problem, we developed a crowd-powered conversational assistant, Chorus, and deployed it to see how users and workers would interact together when mediated by the system. Chorus sophisticatedly conver

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Elvir, Miguel, Avelino J. Gonzalez, Christopher Walls, and Bryan Wilder. "Remembering a Conversation – A Conversational Memory Architecture for Embodied Conversational Agents." Journal of Intelligent Systems 26, no. 1 (2017): 1–21. http://dx.doi.org/10.1515/jisys-2015-0094.

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AbstractThis paper addresses the role of conversational memory in Embodied Conversational Agents (ECAs). It describes an investigation into developing such a memory architecture and integrating it into an ECA. ECAs are virtual agents whose purpose is to engage in conversations with human users, typically through natural language speech. While several works in the literature seek to produce viable ECA dialog architectures, only a few authors have addressed the episodic memory architectures in conversational agents and their role in enhancing their intelligence. In this work, we propose, impleme

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Ford, Nigel. "“Conversational” information systems." Journal of Documentation 61, no. 3 (2005): 362–84. http://dx.doi.org/10.1108/00220410510598535.

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Ren, Pengjie, Zhumin Chen, Zhaochun Ren, Evangelos Kanoulas, Christof Monz, and Maarten De Rijke. "Conversations with Search Engines: SERP-based Conversational Response Generation." ACM Transactions on Information Systems 39, no. 4 (2021): 1–29. http://dx.doi.org/10.1145/3432726.

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In this article, we address the problem of answering complex information needs by conducting conversations with search engines , in the sense that users can express their queries in natural language and directly receive the information they need from a short system response in a conversational manner. Recently, there have been some attempts towards a similar goal, e.g., studies on Conversational Agent s (CAs) and Conversational Search (CS). However, they either do not address complex information needs in search scenarios or they are limited to the development of conceptual frameworks and/or la

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Yan, Rui, Weiheng Liao, Dongyan Zhao, and Ji-Rong Wen. "Multi-Response Awareness for Retrieval-Based Conversations: Respond with Diversity via Dynamic Representation Learning." ACM Transactions on Information Systems 39, no. 4 (2021): 1–29. http://dx.doi.org/10.1145/3470450.

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Conversational systems now attract great attention due to their promising potential and commercial values. To build a conversational system with moderate intelligence is challenging and requires big (conversational) data, as well as interdisciplinary techniques. Thanks to the prosperity of the Web, the massive data available greatly facilitate data-driven methods such as deep learning for human-computer conversational systems. In general, retrieval-based conversational systems apply various matching schema between query utterances and responses, but the classic retrieval paradigm suffers from

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Lipani, Aldo, Ben Carterette, and Emine Yilmaz. "How Am I Doing?: Evaluating Conversational Search Systems Offline." ACM Transactions on Information Systems 39, no. 4 (2021): 1–22. http://dx.doi.org/10.1145/3451160.

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As conversational agents like Siri and Alexa gain in popularity and use, conversation is becoming a more and more important mode of interaction for search. Conversational search shares some features with traditional search, but differs in some important respects: conversational search systems are less likely to return ranked lists of results (a SERP), more likely to involve iterated interactions, and more likely to feature longer, well-formed user queries in the form of natural language questions. Because of these differences, traditional methods for search evaluation (such as the Cranfield pa

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Arabshahi, Forough, Jennifer Lee, Mikayla Gawarecki, Kathryn Mazaitis, Amos Azaria, and Tom Mitchell. "Conversational Neuro-Symbolic Commonsense Reasoning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (2021): 4902–11. http://dx.doi.org/10.1609/aaai.v35i6.16623.

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In order for conversational AI systems to hold more natural and broad-ranging conversations, they will require much more commonsense, including the ability to identify unstated presumptions of their conversational partners. For example, in the command "If it snows at night then wake me up early because I don't want to be late for work" the speaker relies on commonsense reasoning of the listener to infer the implicit presumption that they wish to be woken only if it snows enough to cause traffic slowdowns. We consider here the problem of understanding such imprecisely stated natural language co

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McTear, Michael. "Conversational AI: Dialogue Systems, Conversational Agents, and Chatbots." Synthesis Lectures on Human Language Technologies 13, no. 3 (2020): 1–251. http://dx.doi.org/10.2200/s01060ed1v01y202010hlt048.

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Hans, Sikander, Balwinder Kumar, Vivek Parihar, and Sukhpreet singh. "Human-AI Collaboration: Understanding User Trust in ChatGPT Conversations." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem27929.

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This research paper delves into the critical dimension of Human-AI Collaboration, with a specific focus on unraveling the intricacies of user trust in ChatGPT conversations. In an era marked by increasing AI integration into various aspects of human life, understanding and fostering user trust in conversational AI systems like ChatGPT is essential for effective collaboration. The study employs a comprehensive approach, investigating metrics for trust measurement, analyzing user experiences, and exploring the factors that influence trust. By examining the evolving impact of trust on collaborati

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Kandoi, Raunak, Deepali Dixit, Mihul Tyagi, and Raghuraj Singh Yadav. "Conversational AI." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 769–75. http://dx.doi.org/10.22214/ijraset.2024.58787.

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Abstract: Conversational AI systems are becoming increasingly popular across many industries and are transforming the way people interact with technology. For a more authentic, human-like connection and a smooth user experience, these systems should combine text-based interactions with multimodal capabilities. The authors of this work suggest a new approach to improving conversational AI systems' usability by combining speech and visual analysis. By combining visual and auditory processing capabilities, AI systems can better understand human inquiries and instructions. Both visual data and spe

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Elbert, Mary, Daniel A. Dinnsen, Paula Swartzlander, and Steven B. Chin. "Generalization to Conversational Speech." Journal of Speech and Hearing Disorders 55, no. 4 (1990): 694–99. http://dx.doi.org/10.1044/jshd.5504.694.

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Although changes in children's phonological systems due to treatment have been documented in single-word testing, changes in conversational speech are less well known. Single-word and conversation samples were analyzed for 10 phonologically disordered children, before and after treatment and 3 months later. Results suggest that for most of the children, there were system-changes in both single words and in conversational speech. It appears that many phonologically disordered children are able to extend their correct production to conversation without direct treatment on spontaneous speech.

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Thomas, Paul, Mary Czerwinksi, Daniel Mcduff, and Nick Craswell. "Theories of Conversation for Conversational IR." ACM Transactions on Information Systems 39, no. 4 (2021): 1–23. http://dx.doi.org/10.1145/3439869.

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Conversational information retrieval is a relatively new and fast-developing research area, but conversation itself has been well studied for decades. Researchers have analysed linguistic phenomena such as structure and semantics but also paralinguistic features such as tone, body language, and even the physiological states of interlocutors. We tend to treat computers as social agents—especially if they have some humanlike features in their design—and so work from human-to-human conversation is highly relevant to how we think about the design of human-to-computer applications. In this article,

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Vuong, Tung, Salvatore Andolina, Giulio Jacucci, and Tuukka Ruotsalo. "Spoken Conversational Context Improves Query Auto-completion in Web Search." ACM Transactions on Information Systems 39, no. 3 (2021): 1–32. http://dx.doi.org/10.1145/3447875.

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Web searches often originate from conversations in which people engage before they perform a search. Therefore, conversations can be a valuable source of context with which to support the search process. We investigate whether spoken input from conversations can be used as a context to improve query auto-completion. We model the temporal dynamics of the spoken conversational context preceding queries and use these models to re-rank the query auto-completion suggestions. Data were collected from a controlled experiment and comprised conversations among 12 participant pairs conversing about movi

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Vanderveken, Daniel. "Towards a Formal Pragmatics of Discourse." International Review of Pragmatics 5, no. 1 (2013): 34–69. http://dx.doi.org/10.1163/18773109-13050102.

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Could we enrich speech-act theory to deal with discourse? Wittgenstein and Searle pointed out difficulties. Most conversations lack a conversational purpose, they require collective intentionality, their background is indefinitely open, irrelevant and infelicitous utterances do not prevent conversations to continue, etc. Like Wittgenstein and Searle I am sceptic about the possibility of a general theory of all kinds of language-games. In my view, the single primary purpose of discourse pragmatics is to analyse the structure and dynamics of language-games whose type is provided with an internal

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Adewumi, Tosin, Foteini Liwicki, and Marcus Liwicki. "Vector Representations of Idioms in Conversational Systems." Sci 4, no. 4 (2022): 37. http://dx.doi.org/10.3390/sci4040037.

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In this study, we demonstrate that an open-domain conversational system trained on idioms or figurative language generates more fitting responses to prompts containing idioms. Idioms are a part of everyday speech in many languages and across many cultures, but they pose a great challenge for many natural language processing (NLP) systems that involve tasks such as information retrieval (IR), machine translation (MT), and conversational artificial intelligence (AI). We utilized the Potential Idiomatic Expression (PIE)-English idiom corpus for the two tasks that we investigated: classification a

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Thompson, C. A., M. H. Goker, and P. Langley. "A Personalized System for Conversational Recommendations." Journal of Artificial Intelligence Research 21 (March 1, 2004): 393–428. http://dx.doi.org/10.1613/jair.1318.

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Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquir

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M., N. O. Sadiku, R. Nelatury S., and Musa S.M. "AI in Chatbots: A Primer." Journal of Scientific and Engineering Research 8, no. 2 (2021): 16–22. https://doi.org/10.5281/zenodo.10574855.

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<strong>Abstract </strong>A chatbot is a computer program that imitates human conversation.  It will usually respond in a conversational style, and it may carry out actions in response to your conversation.  Designed to simulate the way a human would behave as a conversational partner, chatbot systems typically require continuous tuning and testing. Amazon, Apple, Google, Microsoft, and Slack support chatbots. This paper provides a primer on artificial intelligence-based chatbots.

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Li, Yongqi, Wenjie Li, and Liqiang Nie. "Dynamic Graph Reasoning for Conversational Open-Domain Question Answering." ACM Transactions on Information Systems 40, no. 4 (2022): 1–24. http://dx.doi.org/10.1145/3498557.

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In recent years, conversational agents have provided a natural and convenient access to useful information in people’s daily life, along with a broad and new research topic, conversational question answering (QA). On the shoulders of conversational QA, we study the conversational open-domain QA problem, where users’ information needs are presented in a conversation and exact answers are required to extract from the Web. Despite its significance and value, building an effective conversational open-domain QA system is non-trivial due to the following challenges: (1) precisely understand conversa

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Chen, Yu-Chuan, and Hen-Hsen Huang. "Exploring Conversational Adaptability: Assessing the Proficiency of Large Language Models in Dynamic Alignment with Updated User Intent." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 22 (2025): 23642–50. https://doi.org/10.1609/aaai.v39i22.34534.

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This paper presents a practical problem in dialogue systems: the capability to adapt to changing user intentions and resolve inconsistencies in conversation histories. It is crucial in scenarios like train ticket booking, where travel plans often change dynamically. Notwithstanding the advancements in NLP and large language models (LLMs), these systems struggle with real-time information updates during conversations. We introduce a specialized dataset to evaluate LLM-based chatbots on such conversational adaptability by asking a broad range of open-domain questions, focusing on scenarios where

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B, Mr DHANUSH. "CHATBOT USING LARGE LANGUAGE MODEL." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34001.

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The concept of Natural Language Processing has seen a remarkable advancement in the recent years. This remarkable advancement was particularly with the development of Large Language Models (LLM). Large Language Models are used to develop a human like conversations. This LLM is a part of Natural Language Processing which focuses on enabling computers to understand, interpret, and generate human language. The existing system of chatbots does not generate human like responses. The proposed system of chatbots uses the power of Large Language Models to generate more human like responses, providing

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Chen, Keyu, and Shiliang Sun. "CP-Rec: Contextual Prompting for Conversational Recommender Systems." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 12635–43. http://dx.doi.org/10.1609/aaai.v37i11.26487.

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The conversational recommender system (CRS) aims to provide high-quality recommendations through interactive dialogues. However, previous CRS models have no effective mechanisms for task planning and topic elaboration, and thus they hardly maintain coherence in multi-task recommendation dialogues. Inspired by recent advances in prompt-based learning, we propose a novel contextual prompting framework for dialogue management, which optimizes prompts based on context, topics, and user profiles. Specifically, we develop a topic controller to sequentially plan the subtasks, and a prompt search modu

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Coppola, Riccardo, and Luca Ardito. "Quality Assessment Methods for Textual Conversational Interfaces: A Multivocal Literature Review." Information 12, no. 11 (2021): 437. http://dx.doi.org/10.3390/info12110437.

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The evaluation and assessment of conversational interfaces is a complex task since such software products are challenging to validate through traditional testing approaches. We conducted a systematic Multivocal Literature Review (MLR), on five different literature sources, to provide a view on quality attributes, evaluation frameworks, and evaluation datasets proposed to provide aid to the researchers and practitioners of the field. We came up with a final pool of 118 contributions, including grey (35) and white literature (83). We categorized 123 different quality attributes and metrics under

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Goh, Ong Sing, Chun Che Fung, Kok Wai Wong, and Arnold Depickere. "Embodied Conversational Agents for H5N1 Pandemic Crisis." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 3 (2007): 282–88. http://dx.doi.org/10.20965/jaciii.2007.p0282.

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This paper presents a novel framework for modeling embodied conversational agent for crisis communication focusing on the H5N1 pandemic crisis. Our system aims to cope with the most challenging issue on the maintenance of an engaging while convincing conversation. What primarily distinguishes our system from other conversational agent systems is that the human-computer conversation takes place within the context of H5N1 pandemic crisis. A Crisis Communication Network, called CCNet, is established based on a novel algorithm incorporating natural language query and embodied conversation agent si

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Reddy, Siva, Danqi Chen, and Christopher D. Manning. "CoQA: A Conversational Question Answering Challenge." Transactions of the Association for Computational Linguistics 7 (November 2019): 249–66. http://dx.doi.org/10.1162/tacl_a_00266.

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Humans gather information through conversations involving a series of interconnected questions and answers. For machines to assist in information gathering, it is therefore essential to enable them to answer conversational questions. We introduce CoQA, a novel dataset for building Conversational Question Answering systems. Our dataset contains 127k questions with answers, obtained from 8k conversations about text passages from seven diverse domains. The questions are conversational, and the answers are free-form text with their corresponding evidence highlighted in the passage. We analyze CoQA

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Pieraccini, Roberto, Krishna Dayanidhi, Jonathan Bloom, et al. "Multimodal conversational systems for automobiles." Communications of the ACM 47, no. 1 (2004): 47. http://dx.doi.org/10.1145/962081.962104.

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Sugiyama, Hiroaki, Ryuichiro Higashinaka, and Toyomi Meguro. "Towards User-friendly Conversational Systems." NTT Technical Review 14, no. 11 (2016): 25–29. http://dx.doi.org/10.53829/ntr201611fa4.

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Trippas, Johanne R. "Spoken conversational search." ACM SIGIR Forum 53, no. 2 (2019): 106–7. http://dx.doi.org/10.1145/3458553.3458570.

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Speech-based web search where no keyboard or screens are available to present search engine results is becoming ubiquitous, mainly through the use of mobile devices and intelligent assistants such as Apple's HomePod, Google Home, or Amazon Alexa. Currently, these intelligent assistants do not maintain a lengthy information exchange. They do not track context or present information suitable for an audio-only channel, and do not interact with the user in a multi-turn conversation. Understanding how users would interact with such an audio-only interaction system in multi-turn information seeking

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Young, Tom, Frank Xing, Vlad Pandelea, Jinjie Ni, and Erik Cambria. "Fusing Task-Oriented and Open-Domain Dialogues in Conversational Agents." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (2022): 11622–29. http://dx.doi.org/10.1609/aaai.v36i10.21416.

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The goal of building intelligent dialogue systems has largely been separately pursued under two paradigms: task-oriented dialogue (TOD) systems, which perform task-specific functions, and open-domain dialogue (ODD) systems, which focus on non-goal-oriented chitchat. The two dialogue modes can potentially be intertwined together seamlessly in the same conversation, as easily done by a friendly human assistant. Such ability is desirable in conversational agents, as the integration makes them more accessible and useful. Our paper addresses this problem of fusing TODs and ODDs in multi-turn dialog

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Lubis, Nurul, Michael Heck, Carel Van Niekerk, and Milica Gasic. "Adaptable Conversational Machines." AI Magazine 41, no. 3 (2020): 28–44. http://dx.doi.org/10.1609/aimag.v41i3.5322.

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In recent years we have witnessed a surge in machine learning methods that provide machines with conversational abilities. Most notably, neural-network–based systems have set the state of the art for difficult tasks such as speech recognition, semantic understanding, dialogue management, language generation, and speech synthesis. Still, unlike for the ancient game of Go for instance, we are far from achieving human-level performance in dialogue. The reasons for this are numerous. One property of human–human dialogue that stands out is the infinite number of possibilities of expressing oneself

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Mahmood Abdullah, Sura, Abbas Mohsin Al-bakry, and Alaa Kadhem Farhan. "Conversational Health Bots for Telemedicine Services: Survey." Iraqi Journal for Computers and Informatics 50, no. 2 (2024): 156–72. https://doi.org/10.25195/ijci.v50i2.508.

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An increasing number of individuals take refuge in telemedicine systems for medical diagnosis and treatment due to their numerous benefits, including reduced healthcare costs, enhanced efficiency, and the ability to treat and prevent a wide range of physical and mental health problems. To improve the health status and clinical findings of older and underserved individuals, healthcare institutions have expanded telemedicine services, integrating them with advanced assisted living systems and environments. Conversational chatbots, or dialogue systems, are software tools designed to emulate human

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Tian, Yingzhong, Yafei Jia, Long Li, Zongnan Huang, and Wenbin Wang. "Research on Modeling and Analysis of Generative Conversational System Based on Optimal Joint Structural and Linguistic Model." Sensors 19, no. 7 (2019): 1675. http://dx.doi.org/10.3390/s19071675.

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Generative conversational systems consisting of a neural network-based structural model and a linguistic model have always been considered to be an attractive area. However, conversational systems tend to generate single-turn responses with a lack of diversity and informativeness. For this reason, the conversational system method is further developed by modeling and analyzing the joint structural and linguistic model, as presented in the paper. Firstly, we establish a novel dual-encoder structural model based on the new Convolutional Neural Network architecture and strengthened attention with

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Muhammad Bilal Ahmad Jamil and Duryab Shahzadi. "A systematic review A Conversational interface agent for the export business acceleration." Lahore Garrison University Research Journal of Computer Science and Information Technology 7, no. 02 (2023): 37–49. http://dx.doi.org/10.54692/lgurjcsit.2023.0702430.

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Conversational agents, which understand, respond to, and learn from each interaction using Automatic Speech Recognition (ASR), Natural Language Processing (NLP), Advanced Dialog Management, and Machine Learning (ML), have become more common in recent years. Conversational agents, also referred to as chatbots, are used to have real-time conversations with individuals. As a result, conversational agents are now being used in a variety of sectors, including those in education, healthcare, marketing, customer assistance, and entertainment. Conversational agents, which are frequently used as chatbo

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Shawar, Bayan Abu, and Eric Steven Atwell. "Using corpora in machine-learning chatbot systems." International Journal of Corpus Linguistics 10, no. 4 (2005): 489–516. http://dx.doi.org/10.1075/ijcl.10.4.06sha.

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A chatbot is a machine conversation system which interacts with human users via natural conversational language. Software to machine-learn conversational patterns from a transcribed dialogue corpus has been used to generate a range of chatbots speaking various languages and sublanguages including varieties of English, as well as French, Arabic and Afrikaans. This paper presents a program to learn from spoken transcripts of the Dialogue Diversity Corpus of English, the Minnesota French Corpus, the Corpus of Spoken Afrikaans, the Qur'an Arabic-English parallel corpus, and the British National Co

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Wu, Zeqiu, Ryu Parish, Hao Cheng, et al. "InSCIt: Information-Seeking Conversations with Mixed-Initiative Interactions." Transactions of the Association for Computational Linguistics 11 (May 18, 2023): 453–68. http://dx.doi.org/10.1162/tacl_a_00559.

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Abstract In an information-seeking conversation, a user may ask questions that are under-specified or unanswerable. An ideal agent would interact by initiating different response types according to the available knowledge sources. However, most current studies either fail to or artificially incorporate such agent-side initiative. This work presents InSCIt, a dataset for Information-Seeking Conversations with mixed-initiative Interactions. It contains 4.7K user-agent turns from 805 human-human conversations where the agent searches over Wikipedia and either directly answers, asks for clarificat

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Ganguly, Debasis, Gareth J. F. Jones, Procheta Sen, Manisha Verma, and Dipasree Pal. "Report on supporting and understanding of conversational dialogues workshop (SUD 2021) at WSDM 2021." ACM SIGIR Forum 55, no. 1 (2021): 1–7. http://dx.doi.org/10.1145/3476415.3476420.

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This report describes the workshop on Supporting and Understanding of (multi-party) conversational Dialogues (SUD) organized as a part of the Web Search and Data Mining conference (WSDM) 2021. The aim of SUD workshop was to encourage researchers to investigate automated methods to analyze and understand conversations. We also discuss the release of a dataset that would be useful in IR research on conversations. The dataset was constructed to support the data challenge in SUD workshop and its precursor event - the Retrieval from Conversational Dialogues (RCD) track at the Forum of Information R

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Lin, Chien-Chang, Anna Y. Q. Huang, and Stephen J. H. Yang. "A Review of AI-Driven Conversational Chatbots Implementation Methodologies and Challenges (1999–2022)." Sustainability 15, no. 5 (2023): 4012. http://dx.doi.org/10.3390/su15054012.

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A conversational chatbot or dialogue system is a computer program designed to simulate conversation with human users, especially over the Internet. These chatbots can be integrated into messaging apps, mobile apps, or websites, and are designed to engage in natural language conversations with users. There are also many applications in which chatbots are used for educational support to improve students’ performance during the learning cycle. The recent success of ChatGPT also encourages researchers to explore more possibilities in the field of chatbot applications. One of the main benefits of c

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Asfoura, Evan, Gamal Kassem, Belal Alhuthaifi, and Fozi Belhaj. "Developing Chatbot Conversational Systems & the Future Generation Enterprise Systems." International Journal of Interactive Mobile Technologies (iJIM) 17, no. 10 (2023): 155–75. http://dx.doi.org/10.3991/ijim.v17i10.37851.

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Conversational technology has recently emerged effectively; it helps people in communicating with smart devices such as smartphones by using human language. When they emerged, they enabled and assisted users to perform various functions such as gathering information, conducting transactions, having general conversations, and easily navigating web services and entertainment. They not only have an impact on people in general by improving customer service as they can provide answers to any inquiries but also facilitated navigation by assisting people with disabilities by interacting with a system

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Igarashi, Toshiharu, Katsuya Iijima, Kunio Nitta, and Yu Chen. "Estimation of the Cognitive Functioning of the Elderly by AI Agents: A Comparative Analysis of the Effects of the Psychological Burden of Intervention." Healthcare 12, no. 18 (2024): 1821. http://dx.doi.org/10.3390/healthcare12181821.

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In recent years, an increasing number of studies have begun to use conversational data in spontaneous speech to estimate cognitive function in older people. The targets of spontaneous speech with older people used to be physicians and licensed psychologists, but it is now possible to have conversations with fully automatic AI agents. However, it has not yet been clarified what difference there is in conversational communication with older people when the examiner is a human or an AI agent. This study explored the psychological burden experienced by elderly participants during cognitive functio

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Vishnuvardhan Reddy Goli. "From clicks to conversations: the rise of conversational UI powered by ai." Global Journal of Engineering and Technology Advances 23, no. 3 (2025): 082–90. https://doi.org/10.30574/gjeta.2025.23.3.0172.

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The evolution of user interfaces from traditional click-based interactions to conversational experiences marks a significant paradigm shift in human-computer interaction. Powered by advances in artificial intelligence, particularly natural language processing and large language models, conversational user interfaces (CUIs) enable more intuitive, efficient, and personalized communication between users and digital systems. This article explores the technological foundations driving this transition, the benefits and challenges of conversational UI, and its transformative impact across industries

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Chuang, Hsiu-Min, and Ding-Wei Cheng. "Conversational AI over Military Scenarios Using Intent Detection and Response Generation." Applied Sciences 12, no. 5 (2022): 2494. http://dx.doi.org/10.3390/app12052494.

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With the rise of artificial intelligence, conversational agents (CA) have found use in various applications in the commerce and service industries. In recent years, many conversational datasets have becomes publicly available, most relating to open-domain social conversations. However, it is difficult to obtain domain-specific or language-specific conversational datasets. This work focused on developing conversational systems based on the Chinese corpus over military scenarios. The soldier will need information regarding their surroundings and orders to carry out their mission in an unfamiliar

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Yadav, Sargam, and Abhishek Kaushik. "Do You Ever Get Off Track in a Conversation? The Conversational System’s Anatomy and Evaluation Metrics." Knowledge 2, no. 1 (2022): 55–87. http://dx.doi.org/10.3390/knowledge2010004.

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Conversational systems are now applicable to almost every business domain. Evaluation is an important step in the creation of dialog systems so that they may be readily tested and prototyped. There is no universally agreed upon metric for evaluating all dialog systems. Human evaluation, which is not computerized, is now the most effective and complete evaluation approach. Data gathering and analysis are evaluation activities that need human intervention. In this work, we address the many types of dialog systems and the assessment methods that may be used with them. The benefits and drawbacks o

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Agarwal, Sanchit, Jan Jezabek, Arijit Biswas, Emre Barut, Bill Gao, and Tagyoung Chung. "Building Goal-Oriented Dialogue Systems with Situated Visual Context." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 13149–51. http://dx.doi.org/10.1609/aaai.v36i11.21710.

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Goal-oriented dialogue agents can comfortably utilize the conversational context and understand its users' goals. However, in visually driven user experiences, these conversational agents are also required to make sense of the screen context in order to provide a proper interactive experience. In this paper, we propose a novel multimodal conversational framework where the dialogue agent's next action and their arguments are derived jointly conditioned both on the conversational and the visual context. We demonstrate the proposed approach via a prototypical furniture shopping experience for a m

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Singh, Shiv. "Building Smarter End-Of-Turn Detection for Conversational AI Using Transformer-Based Semantic Models." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47748.

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Abstract This paper presents a novel approach to end-of-turn detection for conversational AI systems, combining traditional voice activity detection (VAD) with semantic understanding through a transformer-based model. End-of-turn detection remains one of the most challenging aspects of creating natural conversational AI interfaces, as current systems rely primarily on silence thresholds that fail to capture the semantic cues humans use to determine speaking turns. Our approach leverages a lightweight transformer model based on the Gemma-3-1b architecture that analyzes transcribed speech in rea

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Vadlamani, Srikanth, and Daksha Borada. "The Role of LLM Agent Apps in Conversational AI." International Journal of Research in Modern Engineering & Emerging Technology 13, no. 4 (2025): 205–23. https://doi.org/10.63345/ijrmeet.org.v13.i4.12.

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The rapid growth of Large Language Models (LLMs) has tremendously boosted the strength of conversational artificial intelligence applications. In this regard, LLM agent applications have become strong tools for automating and individualizing conversations across industries such as customer service, healthcare, education, and entertainment. LLM agent applications use LLMs to have sophisticated, contextually aware conversations, thereby providing users with a natural conversational experience. Though they have great capabilities, there is a massive research gap regarding the distinctive roles an

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