Affect as Information about Users' Attitudes to Conversational Agents

Ptaszyński, Michał; Dybała, Paweł; Higuchi, Shinsuke; Rzepka, Rafał; Araki, Kenji

doi:10.1109/wiiat.2008.192

Cited by 3 publications

(4 citation statements)

References 11 publications

(11 reference statements)

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“…As part of our research on an emotion recognition agent, we have also developed an automatic emotiveness-analysis-based method for dialogue systems evaluation. The chat logs from an experiment with our humor-equipped agents were analyzed using ML-Ask Emotive Elements/Emotive Expressions Analysis Agent, developed by Ptaszynski et al [25,26]. ML-Ask analyzed users' emotive reactions towards both (humor-and non-humor-equipped) agents, searching for positive and negative emotions.…”

Section: Our Research So Farmentioning

confidence: 99%

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Multiagent system for joke generation: Humor and emotions combined in human-agent conversation

Dybała¹,

Ptaszyński²,

Maciejewski³

et al. 2010

Journal of Ambient Intelligence and Smart Environments

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In this paper we present an innovative work on a multiagent joking conversational system. In our research so far we have shown that implementing humor into a chatterbot can visibly improve its performance. The results presented in this paper are the outcome of the next step of our work. They show that a multiagent system, combining a conversational agent, a pun generator and an emotiveness analysis engine, works reasonably well in interactions with users. In the setup used in this research, the emotiveness analysis agent analyses users' utterances and decides whether it is appropriate to tell a pun. Depending on the results of this analysis, the agent chooses either the pun generator, if the decision is that a joke should be told, or the non-humor-equipped agent when the decision is different. Two evaluation experiments were conducted: user (first person) focused and automatic (emotiveness-analysis-based). In both, we compared the performance of the multiagent joking system and a baseline (non-humorous) conversation agent. The results show that in both cases the humor-equipped engine was evaluated as better than the baseline agent. The results are discussed and some ideas for the future are given.

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Section: Our Research So Farmentioning

confidence: 99%

“…Another agent used in this research is Ptaszynski et al's ML-Ask Emotive Elements/Emotive Expressions Analysis System [25,26]. As mentioned above, in this system the emotiveness analysis agent performs two functions:…”

Section: Emotiveness Analysis Agent (Ml-ask)mentioning

confidence: 99%

Multiagent system for joke generation: Humor and emotions combined in human-agent conversation

Dybała¹,

Ptaszyński²,

Maciejewski³

et al. 2010

Journal of Ambient Intelligence and Smart Environments

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“…As shown in Figure 7, most of the users showed more emotions towards Pundalin than towards Modalin, which means that they were generally more emotively involved in the conversation with the system which used humor [20]). …”

Section: General Emotivenessmentioning

confidence: 96%

“…If emotions detected by ML-Ask were positive or changing from negative through neutral (non-emotive) to positive during the whole conversation, the general sentiment towards the system was considered to be positive. If the emotions detected by ML-Ask were negative or changing from positive through neutral to negative during the whole conversation, the general sentiment towards the system was considered to be negative [20].…”

Section: Determining General Emotiveness (Emotive/non-emotive) and 2mentioning

confidence: 99%

Evaluating Subjective Aspects of Hci on an Example of a Non-Task Oriented Conversational System

Dybała

Ptaszyński

Rzepka

et al. 2010

Int. J. Artif. Intell. Tools

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The evaluation of subjective aspects of HCI, such as human-likeness, likeability or users' emotions towards computers is still quite a neglected issue, especially in the field of non-task oriented conversational systems (chatterbots). In this paper we try to bridge this gap by proposing a new methodology of evaluation. The methods presented were tested in our research on humor-equipped chatterbots. We describe them in details, discuss their drawbacks and usability. In one of the presented methods we used an emotiveness analysis system, which itself can be considered an AI tool, as it was used to detect users' emotions towards conversational systems, and to perform their automatic evaluation. We also propose some methods that we have not used yet, which, however, * Corresponding author: paweldybala@media.eng.hokudai.ac.jp (Pawel Dybala) Pawel Dybala, Michal Ptaszynski, Rafal Rzepka, and Kenji Araki 2 seem applicable in this field, such as brain scanning techniques. Finally, we give some ideas that should be addressed in the future.

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Activating Humans with Humor-A Dialogue System That Users Want to Interact with

Dybała

Ptaszyński

Rzepka

et al. 2009

IEICE Trans. Inf. & Syst.

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SUMMARYThe topic of Human Computer Interaction (HCI) has been gathering more and more scientific attention of late. A very important, but often undervalued area in this field is human engagement. That is, a person's commitment to take part in and continue the interaction. In this paper we describe work on a humor-equipped casual conversational system (chatterbot) and investigate the effect of humor on a user's engagement in the conversation. A group of users was made to converse with two systems: one with and one without humor. The chat logs were then analyzed using an emotive analysis system to check user reactions and attitudes towards each system. Results were projected on Russell's two-dimensional emotiveness space to evaluate the positivity/negativity and activation/deactivation of these emotions. This analysis indicated emotions elicited by the humorequipped system were more positively active and less negatively active than by the system without humor. The implications of results and relation between them and user engagement in the conversation are discussed. We also propose a distinction between positive and negative engagement.

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Affect as Information about Users' Attitudes to Conversational Agents

Cited by 3 publications

References 11 publications

Multiagent system for joke generation: Humor and emotions combined in human-agent conversation

Multiagent system for joke generation: Humor and emotions combined in human-agent conversation

Evaluating Subjective Aspects of Hci on an Example of a Non-Task Oriented Conversational System

Activating Humans with Humor-A Dialogue System That Users Want to Interact with

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