An End-to-End Conversational Style Matching Agent

Hoegen, Rens; Aneja, Deepali; McDuff, Daniel; Czerwinski, Mary

doi:10.1145/3308532.3329473

Cited by 44 publications

(20 citation statements)

References 27 publications

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“…We selected Tannen's classification of style to define conversational styles of agents, since recent work has shown its suitability in understanding styles in human-human conversations, and also in human-agent conversations [34]. Moreover, Tannen's classification has served as the basis for aligning the style of an end-to-end voice-based agent with that of an interlocutor [17].…”

Section: )mentioning

confidence: 99%

Improving Worker Engagement Through Conversational Microtask Crowdsourcing

Qiu

Gadiraju

Bozzon

2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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The rise in popularity of conversational agents has enabled humans to interact with machines more naturally. Recent work has shown that crowd workers in microtask marketplaces can complete a variety of human intelligence tasks (HITs) using conversational interfaces with similar output quality compared to the traditional Web interfaces. In this paper, we investigate the effectiveness of using conversational interfaces to improve worker engagement in microtask crowdsourcing. We designed a text-based conversational agent that assists workers in task execution, and tested the performance of workers when interacting with agents having different conversational styles. We conducted a rigorous experimental study on Amazon Mechanical Turk with 800 unique workers, to explore whether the output quality, worker engagement and the perceived cognitive load of workers can be affected by the conversational agent and its conversational styles. Our results show that conversational interfaces can be effective in engaging workers, and a suitable conversational style has potential to improve worker engagement. Our findings have important implications on workflows and task design with regard to better engaging workers in microtask crowdsourcing marketplaces.

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Section: )mentioning

confidence: 99%

Improving Worker Engagement Through Conversational Microtask Crowdsourcing

Qiu

Gadiraju

Bozzon

2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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“…Prior work on human-machine lexical entrainment looked at conversations between humans and physical robots in a shared space, or conversational agents (Hoegen et al, 2019). More generally, the entrainment phenomenon is usually studied over the course of a conversation, to determine if conversation participants imitate each other's conversation styles.…”

Section: Discussionmentioning

confidence: 99%

An Empirical Analysis of Human-Bot Interaction on Reddit

Ma¹,

Lalor

2020

Proceedings of the Sixth Workshop on Noisy User-Generated Text (W-Nut 2020)

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Automated agents ("bots") have emerged as an ubiquitous and influential presence on social media. Bots engage on social media platforms by posting content and replying to other users on the platform. In this work we conduct an empirical analysis of the activity of a single bot on Reddit. Our goal is to determine whether bot activity (in the form of posted comments on the website) has an effect on how humans engage on Reddit. We find that (1) the sentiment of a bot comment has a significant, positive effect on the subsequent human reply, and (2) human Reddit users modify their comment behaviors to overlap with the text of the bot, similar to how humans modify their text to mimic other humans in conversation. Understanding human-bot interactions on social media with relatively simple bots is important for preparing for more advanced bots in the future.

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“…We argue that incorporating such data into the modeling component of a virtual agent and using it to explicitly model these aspects will improve the support agents can giveÐnot only in terms of what it recommends, but also in its interaction with the user (e.g. presenting as an agent with a compatible personality, using an uplifting or comforting voice, and presenting information in a manner that supports the user's decision-making style [13]). Taken one step further, a łhuman digital twinž that mimics users' own preferences decision practices could offer users a transformative reflective lens to support exploration and understanding [3,22].…”

Section: The Visionmentioning

confidence: 99%

Human-Centric Preference Modeling for Virtual Agents

Knijnenburg

Hubig

2020

Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents

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Intelligent virtual agents increasingly use machine learning to model the needs and preferences of their users in order to give personalized support. In this paper we present a vision of łhumancentric modelingž that moves this practice beyond the automated estimation of users' current preferences based on observable behaviors to a modeling process that a) considers users' hidden characteristics rather than observable behaviors, b) models possible futures rather than users' current state, and c) gives users an opportunity to interactively control this modeling process. This vision focuses on the long-term needs of individual humans and a genuine betterment of society. CCS CONCEPTS • Information systems → Decision support systems; • Computing methodologies → Intelligent agents; • Human-centered computing → Human computer interaction (HCI).

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An End-to-End Conversational Style Matching Agent

Cited by 44 publications

References 27 publications

Improving Worker Engagement Through Conversational Microtask Crowdsourcing

Improving Worker Engagement Through Conversational Microtask Crowdsourcing

An Empirical Analysis of Human-Bot Interaction on Reddit

Human-Centric Preference Modeling for Virtual Agents

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