12,550 research outputs found
Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings
We study a symmetric collaborative dialogue setting in which two agents, each
with private knowledge, must strategically communicate to achieve a common
goal. The open-ended dialogue state in this setting poses new challenges for
existing dialogue systems. We collected a dataset of 11K human-human dialogues,
which exhibits interesting lexical, semantic, and strategic elements. To model
both structured knowledge and unstructured language, we propose a neural model
with dynamic knowledge graph embeddings that evolve as the dialogue progresses.
Automatic and human evaluations show that our model is both more effective at
achieving the goal and more human-like than baseline neural and rule-based
models.Comment: ACL 201
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Lyceum: internet voice groupware for distance learning
This paper describes the design, implementation and deployment of Lyceum, a groupware system providing students and tutors with real time voice conferencing and visual workspace tools, over the standard internet. Lyceum uses a Java client/server architecture to tackle a formidable set of networking requirements: multi-way voice communication with synchronous shared displays, scalable to hundreds of simultaneous users, running over normal modem connections via unknown internet service providers, on home PCs. Additionally, the design had to support multiple courses with different requirements. We describe the interdisciplinary requirements analysis, and iterative design process, by which an academic course team was able to specify and evaluate prototypes. We present the systemรญs architecture, describe the technical successes and failures from Lyceumรญs first large scale deployment, and summarise its affordances for interaction and learning
Informal Online Decision Making: Current Practices and Support System Design
Existing group decision support systems are too complex to support lightweight, informal decision making made popular by the amount of information available on the Web. From an examination of related work, an online survey and a formative study to examine how people currently use the Web for decision support, we present a set of design recommendations towards the development of an informal Web decision support tool
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Innovating Pedagogy 2015: Open University Innovation Report 4
This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This fourth report proposes ten innovations that are already in currency but have not yet had a profound influence on education. To produce it, a group of academics at the Institute of Educational Technology in The Open University collaborated with researchers from the Center for Technology in Learning at SRI International. We proposed a long list of new educational terms, theories, and practices. We then pared these down to ten that have the potential to provoke major shifts in educational practice, particularly in post-school education. Lastly, we drew on published and unpublished writings to compile the ten sketches of new pedagogies that might transform education. These are summarised below in an approximate order of immediacy and timescale to widespread implementation
Social media as a data gathering tool for international business qualitative research: opportunities and challenges
Lusophone African (LA) multinational enterprises (MNEs) are becoming a significant pan-African and global economic force regarding their international presence and influence. However, given the extreme poverty and lack of development in their home markets, many LA enterprises seeking to internationalize lack resources and legitimacy in international markets. Compared to higher income emerging markets, Lusophone enterprises in Africa face more significant challenges in their internationalization efforts. Concomitantly, conducting significant international business (IB) research in these markets to understand these MNEs internationalization strategies can be a very daunting task. The fast-growing rise of social media on the Internet, however, provides an opportunity for IB researchers to examine new phenomena in these markets in innovative ways. Unfortunately, for various reasons, qualitative researchers in IB have not fully embraced this opportunity. This article studies the use of social media in qualitative research in the field of IB. It offers an illustrative case based on qualitative research on internationalization modes of LAMNEs conducted by the authors in Angola and Mozambique using social media to identify and qualify the population sample, as well as interact with subjects and collect data. It discusses some of the challenges of using social media in those regions of Africa and suggests how scholars can design their studies to capitalize on social media and corresponding data as a tool for qualitative research. This article underscores the potential opportunities and challenges inherent in the use of social media in IB-oriented qualitative research, providing recommendations on how qualitative IB researchers can design their studies to capitalize on data generated by social media.https://doi.org/10.1080/15475778.2019.1634406https://doi.org/10.1080/15475778.2019.1634406https://doi.org/10.1080/15475778.2019.1634406https://doi.org/10.1080/15475778.2019.1634406Accepted manuscriptPublished versio
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Interactive task design: Metachat and the whole learner
In this chapter the focus is on conversations about language between adult learners online, in synchronous and asynchronous postings. Socio-affective and social-semiotic perspectives are used, thus distancing the work somewhat from cognitive ways of looking at tasks. Because adults come to the task with diverse knowledge of both L2 and L1, the expectation is that metalinguistic interaction will enable them to swap expert and novice roles with each other within the constantly changing dynamics of the classroom. This if shown to be the case would advance an educational agenda favouring learner-directedness. Secondly, as metalinguistic conversations develop in directions that the learners feel like following, a greater degree of contingency can arise. This is considered in this paper as motivational for adults, and also as progressive, following van Lier (1996: 180) for whom in a contingent conversation "the agenda is shared by all participants and educational reality may be transformed". However, in seeking to satisfy his condition of contingency, the problem of designing tasks for greater spontaneity proves difficult. Therefore this study provide an ethnographic account of metalinguistic conversations by learners engaged in an online task, Simuligne, designed to address this difficulty. After studying data from the project forums, chat rooms and emails, we introduce a new perspective on the function of these conversations, which holds pointers for task design
์ธ๊ณต์ง๋ฅ๊ณผ ๋ํํ๊ธฐ: ์ผ๋์ผ ๊ทธ๋ฆฌ๊ณ ๊ทธ๋ฃน ์์ฉ์์ฉ์ ์ํ ๋ํํ ์์ด์ ํธ ์์คํ ๊ฐ๋ฐ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ์ฌํ๊ณผํ๋ํ ์ธ๋ก ์ ๋ณดํ๊ณผ, 2022.2. ์ด์คํ."์ธ๊ฐ-์ปดํจํฐ ์ํธ์์ฉ"๊ณผ "์ฌ์ฉ์ ๊ฒฝํ"์ ๋์ด, "์ธ๊ฐ-์ธ๊ณต์ง๋ฅ ์ํธ์์ฉ" ๊ทธ๋ฆฌ๊ณ "์๊ณ ๋ฆฌ์ฆ ๊ฒฝํ"์ ์๋๊ฐ ๋๋ํ๊ณ ์๋ค. ๊ธฐ์ ์ ๋ฐ์ ์ ์ฐ๋ฆฌ๊ฐ ์์ฌ์ํตํ๊ณ ํ์
ํ๋ ๋ฐฉ์์ ํจ๋ฌ๋ค์์ ์ ํํ๋ค. ๊ธฐ๊ณ ์์ด์ ํธ๋ ์ธ๊ฐ ์ปค๋ฎค๋์ผ์ด์
์์ ์ ๊ทน์ ์ด๋ฉฐ ์ฃผ๋์ ์ธ ์ญํ ์ ์ํํ๋ค.
ํ์ง๋ง ํจ๊ณผ์ ์ธ AI ๊ธฐ๋ฐ ์ปค๋ฎค๋์ผ์ด์
๊ณผ ํ ๋ก ์์คํ
๋์์ธ์ ๋ํ ์ดํด์ ๋
ผ์๋ ๋ถ์กฑํ ๊ฒ์ด ์ฌ์ค์ด๋ค. ์ด์ ๋ณธ ์ฐ๊ตฌ๋ ์ธ๊ฐ-์ปดํจํฐ ์ํธ์์ฉ์ ๊ด์ ์์ ๋ค์ํ ํํ์ ์ปค๋ฎค๋์ผ์ด์
์ ์ง์ํ ์ ์๋ ๊ธฐ์ ์ ๋ฐฉ๋ฒ์ ํ์ํ๋ ๊ฒ์ ๋ชฉํ๋ก ํ๋ค. ์ด๋ฅผ ์ํด ์ ์๋ ์ผ๋์ผ ๊ทธ๋ฆฌ๊ณ ๊ทธ๋ฃน ์ํธ์์ฉ์ ์ง์ํ๋ ๋ํํ ์์ด์ ํธ๋ฅผ ์ ์ํ๋ค. ๊ตฌ์ฒด์ ์ผ๋ก ๋ณธ ์ฐ๊ตฌ๋ 1) ์ผ๋์ผ ์ํธ์์์์ ์ฌ์ฉ์ ๊ด์ฌ๋ฅผ ๋์ด๋ ๋ํํ ์์ด์ ํธ, 2) ์ผ์์ ์ธ ์์
๊ทธ๋ฃน ํ ๋ก ์ ์ง์ํ๋ ์์ด์ ํธ, 3) ์์ ํ ๋ก ์ ๊ฐ๋ฅํ๊ฒ ํ๋ ์์ด์ ํธ๋ฅผ ๋์์ธ ๋ฐ ๊ฐ๋ฐํ๊ณ ๊ทธ ํจ๊ณผ๋ฅผ ์ ๋์ ๊ทธ๋ฆฌ๊ณ ์ ์ฑ์ ์ผ๋ก ๊ฒ์ฆํ๋ค. ์์คํ
์ ๋์์ธํจ์ ์์ด์ ์ธ๊ฐ-์ปดํจํฐ ์ํธ์์ฉ๋ฟ ์๋๋ผ, ์ปค๋ฎค๋์ผ์ด์
ํ, ์ฌ๋ฆฌํ, ๊ทธ๋ฆฌ๊ณ ๋ฐ์ดํฐ ๊ณผํ์ ์ ๋ชฉํ ๋คํ์ ์ ์ ๊ทผ ๋ฐฉ์์ด ์ ์ฉ๋์๋ค.
์ฒซ ๋ฒ์งธ ์ฐ๊ตฌ๋ ์ผ๋์ผ ์ํธ์์ฉ ์ํฉ์์ ์ฌ์ฉ์์ ๊ด์ฌ ์ฆ์ง์ ์ํ ๋ํํ ์์ด์ ํธ์ ํจ๊ณผ๋ฅผ ๊ฒ์ฆํ๋ค. ์ค๋ฌธ์กฐ์ฌ๋ผ๋ ๋งฅ๋ฝ์์ ์ํ๋ ์ด ์ฐ๊ตฌ๋ ์น ์ค๋ฌธ์กฐ์ฌ์์ ์๋ต์์ ๋ถ์ฑ์ค๋ก ์ธํด ๋ฐ์ํ๋ ์๋ต ๋ฐ์ดํฐ ํ์ง์ ๋ฌธ์ ๋ฅผ ๊ทน๋ณตํ๊ธฐ ์ํ ์๋ก์ด ์ธํฐ๋์
๋ฐฉ๋ฒ์ผ๋ก ํ
์คํธ ๊ธฐ๋ฐ ๋ํํ ์์ด์ ํธ์ ๊ฐ๋ฅ์ฑ์ ํ์ํ๋ ๊ฒ์ ๋ชฉํ๋ก ํ๋ค. ์ด๋ฅผ ์ํด 2 (์ธํฐํ์ด์ค: ์น ๅฐ ์ฑ๋ด) X 2 (๋ํ ์คํ์ผ: ํฌ๋ฉ ๅฐ ์บ์ฅฌ์ผ) ์คํ์ ์งํํ์ผ๋ฉฐ, ๋ง์กฑํ ์ด๋ก ์ ๊ทผ๊ฑฐํ์ฌ ์๋ต ๋ฐ์ดํฐ์ ํ์ง์ ํ๊ฐํ๋ค. ๊ทธ ๊ฒฐ๊ณผ, ์ฑ๋ด ์ค๋ฌธ์กฐ์ฌ์ ์ฐธ์ฌ์๊ฐ ์น ์ค๋ฌธ์กฐ์ฌ์ ์ฐธ์ฌ์๋ณด๋ค ๋ ๋์ ์์ค์ ๊ด์ฌ๋ฅผ ๋ณด์ด๊ณ , ๊ฒฐ๊ณผ์ ์ผ๋ก ๋ ๋์ ํ์ง์ ๋ฐ์ดํฐ๋ฅผ ์์ฑํ๋ ๊ฒ์ ํ์ธํ ์ ์์๋ค. ํ์ง๋ง ์ด๋ฐ ์ฑ๋ด์ ๋ฐ์ดํฐ ํ์ง์ ๋ํ ํจ๊ณผ๋ ์ฑ๋ด์ด ์น๊ตฌ ๊ฐ๊ณ ์บ์ฅฌ์ผํ ๋ํ์ฒด๋ฅผ ์ฌ์ฉํ ๋๋ง ๋ํ๋ฌ๋ค. ์ด ๊ฒฐ๊ณผ๋ ๋ํํ ์ธํฐ๋ํฐ๋นํฐ๊ฐ ์ธํฐํ์ด์ค๋ฟ ์๋๋ผ ๋ํ ์คํ์ผ์ด๋ผ๋ ํจ๊ณผ์ ์ธ ๋ฉ์ธ์ง ์ ๋ต์ ๋๋ฐํ ๋ ๋ฐ์ํ๋ ๊ฒ์ ์๋ฏธํ๋ค.
๋ ๋ฒ์งธ ์ฐ๊ตฌ๋ ์ผ์์ ์ธ ์์
์ฑํ
๊ทธ๋ฃน์์ ์ง๋จ์ ์์ฌ๊ฒฐ์ ๊ณผ์ ๊ณผ ํ ๋ก ์ ์ง์ํ๋ ๋ํํ ์์คํ
์ ๋ํ ๊ฒ์ด๋ค. ์ด๋ฅผ ์ํด GroupfeedBot์ด๋ผ๋ ๋ํํ ์์ด์ ํธ๋ฅผ ์ ์ํ์์ผ๋ฉฐ, GroupfeedBot์ (1) ํ ๋ก ์๊ฐ์ ๊ด๋ฆฌํ๊ณ , (2) ๊ตฌ์ฑ์๋ค์ ๊ท ๋ฑํ ์ฐธ์ฌ๋ฅผ ์ด์งํ๋ฉฐ, (3) ๊ตฌ์ฑ์๋ค์ ๋ค์ํ ์๊ฒฌ์ ์์ฝ ๋ฐ ์กฐ์งํํ๋ ๊ธฐ๋ฅ์ ๊ฐ๊ณ ์๋ค. ํด๋น ์์ด์ ํธ๋ฅผ ํ๊ฐํ๊ธฐ ์ํด ๋ค์ํ ํ์คํฌ (์ถ๋ก , ์์ฌ๊ฒฐ์ , ์์ ํ ๋ก , ๋ฌธ์ ํด๊ฒฐ ๊ณผ์ )์ ๊ทธ๋ฃน ๊ท๋ชจ(์๊ท๋ชจ, ์ค๊ท๋ชจ)์ ๊ดํ์ฌ ์ฌ์ฉ์ ์กฐ์ฌ๋ฅผ ์ํํ๋ค. ๊ทธ ๊ฒฐ๊ณผ ์๊ฒฌ์ ๋ค์์ฑ ์ธก๋ฉด์์ GroupfeedBot์ผ๋ก ํ ๋ก ํ ์ง๋จ์ด ๊ธฐ๋ณธ ์์ด์ ํธ์ ํ ๋ก ํ ์ง๋จ๋ณด๋ค ๋ ๋ค์ํ ์๊ฒฌ์ ์์ฑํ์ง๋ง ์ฐ์ถ๋ ๊ฒฐ๊ณผ์ ํ์ง๊ณผ ๋ฉ์์ง ์์ ์์ด์๋ ์ฐจ์ด๊ฐ ์๋ ๊ฒ์ ํ์ธํ ์ ์์๋ค. ๊ท ๋ฑํ ์ฐธ์ฌ์ ๋ํ GroupfeedBot์ ํจ๊ณผ๋ ํ์คํฌ์ ํน์ฑ์ ๋ฐ๋ผ ๋ค๋ฅด๊ฒ ๋ํ๋ฌ๋๋ฐ, ํนํ ์์ ํ ๋ก ๊ณผ์ ์์ GroupfeedBot์ด ์ฐธ์ฌ์๋ค์ ๊ท ๋ฑํ ์ฐธ์ฌ๋ฅผ ์ด์งํ๋ค.
์ธ ๋ฒ์งธ ์ฐ๊ตฌ๋ ์์ ํ ๋ก ์ ์ง์ํ๋ ๋ํํ ์์คํ
์ ๋ํ ๊ฒ์ด๋ค. ์ธ ๋ฒ์งธ ์ฐ๊ตฌ์์ ๊ฐ๋ฐ๋ DebateBot์ GroupfeeedBot๊ณผ ๋ฌ๋ฆฌ ๋ ์ง์งํ ์ฌํ์ ๋งฅ๋ฝ์์ ์ ์ฉ๋์๋ค. DebateBot์ (1) ์๊ฐํ๊ธฐ-์ง์ง๊ธฐ-๊ณต์ ํ๊ธฐ (Think-Pair-Share) ์ ๋ต์ ๋ฐ๋ผ ํ ๋ก ์ ๊ตฌ์กฐํํ๊ณ , (2) ๊ณผ๋ฌตํ ํ ๋ก ์์๊ฒ ์๊ฒฌ์ ์์ฒญํจ์ผ๋ก์จ ๋๋ฑํ ์ฐธ์ฌ๋ฅผ ์ด์งํ๋ ๋ ๊ฐ์ง ์ฃผ์ ๊ธฐ๋ฅ์ ์ํํ๋ค. ์ฌ์ฉ์ ํ๊ฐ ๊ฒฐ๊ณผ DebateBot์ ๊ทธ๋ฃน ์ํธ์์ฉ์ ๊ฐ์ ํจ์ผ๋ก์จ ์ฌ์ ํ ๋ก ์ ๊ฐ๋ฅํ๊ฒ ํ๋ค. ํ ๋ก ๊ตฌ์กฐํ๋ ํ ๋ก ์ ์ง์ ๊ธ์ ์ ์ธ ํจ๊ณผ๋ฅผ ๋ฐํํ์๊ณ , ์ฐธ์ฌ์ ์ด์ง์ ์ง์ ํ ํฉ์ ๋๋ฌ์ ๊ธฐ์ฌํ์์ผ๋ฉฐ, ๊ทธ๋ฃน ๊ตฌ์ฑ์๋ค์ ์ฃผ๊ด์ ๋ง์กฑ๋๋ฅผ ํฅ์ํ๋ค.
๋ณธ ์ฐ๊ตฌ๋ ์ด ์ธ ๊ฐ์ง ์ฐ๊ตฌ์ ๊ฒฐ๊ณผ๋ค์ ๋ฐํ์ผ๋ก ์ธ๊ฐ-์ธ๊ณต์ง๋ฅ ์ปค๋ฎค๋์ผ์ด์
์ ๋ํ ๋ค์ํ ์์ฌ์ ๋ค์ ๋์ถํ์์ผ๋ฉฐ, ์ด๋ฅผ TAMED (Task-Agent-Message-Information Exchange-Relationship Dynamics) ๋ชจ๋ธ๋ก ์ ๋ฆฌํ์๋ค.The advancements in technology shift the paradigm of how individuals communicate and collaborate. Machines play an active role in human communication. However, we still lack a generalized understanding of how exactly to design effective machine-driven communication and discussion systems. How should machine agents be designed differently when interacting with a single user as opposed to when interacting with multiple users? How can machine agents be designed to drive user engagement during dyadic interaction? What roles can machine agents perform for the sake of group interaction contexts? How should technology be implemented in support of the group decision-making process and to promote group dynamics? What are the design and technical issues which should be considered for the sake of creating human-centered interactive systems?
In this thesis, I present new interactive systems in the form of a conversational agent, or a chatbot, that facilitate dyadic and group interactions. Specifically, I focus on: 1) a conversational agent to engage users in dyadic communication, 2) a chatbot called GroupfeedBot that facilitates daily social group discussion, 3) a chatbot called DebateBot that enables deliberative discussion. My approach to research is multidisciplinary and informed by not only in HCI, but also communication, psychology and data science. In my work, I conduct in-depth qualitative inquiry and quantitative data analysis towards understanding issues that users have with current systems, before developing new computational techniques that meet those user needs. Finally, I design, build, and deploy systems that use these techniques to the public in order to achieve real-world impact and to study their use by different usage contexts.
The findings of this thesis are as follows. For a dyadic interaction, participants interacting with a chatbot system were more engaged as compared to those with a static web system. However, the conversational agent leads to better user engagement only when the messages apply a friendly, human-like conversational style. These results imply that the chatbot interface itself is not quite sufficient for the purpose of conveying conversational interactivity. Messages should also be carefully designed to convey such.
Unlike dyadic interactions, which focus on message characteristics, other elements of the interaction should be considered when designing agents for group communication. In terms of messages, it is important to synthesize and organize information given that countless messages are exchanged simultaneously. In terms of relationship dynamics, rather than developing a rapport with a single user, it is essential to understand and facilitate the dynamics of the group as a whole. In terms of task performance, technology should support the group's decision-making process by efficiently managing the task execution process.
Considering the above characteristics of group interactions, I created the chatbot agents that facilitate group communication in two different contexts and verified their effectiveness. GroupfeedBot was designed and developed with the aim of enhancing group discussion in social chat groups. GroupfeedBot possesses the feature of (1) managing time, (2) encouraging members to participate evenly, and (3) organizing the membersโ diverse opinions. The group which discussed with GroupfeedBot tended to produce more diverse opinions compared to the group discussed with the basic chatbot. Some effects of GroupfeedBot varied by the task's characteristics. GroupfeedBot encouraged the members to contribute evenly to the discussions, especially for the open-debating task.
On the other hand, DebateBot was designed and developed to facilitate deliberative discussion. In contrast to GroupfeedBot, DebateBot was applied to more serious and less casual social contexts. Two main features were implemented in DebateBot: (1) structure discussion and (2) request opinions from reticent discussants.This work found that a chatbot agent which structures discussions and promotes even participation can improve discussions, resulting in higher quality deliberative discussion. Overall, adding structure to the discussion positively influenced the discussion quality, and the facilitation helped groups reach a genuine consensus and improved the subjective satisfaction of the group members.
The findings of this thesis reflect the importance of understanding human factors in designing AI-infused systems. By understanding the characteristics of individual humans and collective groups, we are able to place humans at the heart of the system and utilize AI technology in a human-friendly way.1. Introduction
1.1 Background
1.2 Rise of Machine Agency
1.3 Theoretical Framework
1.4 Research Goal
1.5 Research Approach
1.6 Summary of Contributions
1.7 Thesis Overview
2. Related Work
2.1 A Brief History of Conversational Agents
2.2 TAMED Framework
3. Designing Conversational Agents for Dyadic Interaction
3.1 Background
3.2 Related Work
3.3 Method
3.4 Results
3.5 Discussion
3.6 Conclusion
4. Designing Conversational Agents for Social Group Discussion
4.1 Background
4.2 Related Work
4.3 Needfinding Survey for Facilitator Chatbot Agent
4.4 GroupfeedBot: A Chatbot Agent For Facilitating Discussion in
Group Chats
4.5 Qualitative Study with Small-Sized Group
4.6 User Study With Medium-Sized Group
4.7 Discussion
4.8 Conclusion
5. Designing Conversational Agents for Deliberative Group Discussion
5.1 Background
5.2 Related Work
5.3 DebateBot
5.4 Method
5.5 Results
5.6 Discussion and Design Implications
5.7 Conclusion
6. Discussion
6.1 Designing Conversational Agents as a Communicator
6.2 Design Guidelines Based on TAMED Model
6.3 Technical Considerations
6.4 Human-AI Collaborative System
7. Conclusion
7.1 Research Summary
7.2 Summary of Contributions
7.3 Future Work
7.4 Conclusion๋ฐ
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