Definition, conceptualisation and measurement of trust

This report documents the program and the outcomes of Dagstuhl Seminar 21381 "Conversational Agent as Trustworthy Autonomous System (Trust-CA)". First, we present the abstracts of the talks delivered by the Seminar’s attendees. Then we report on the origin and process of our six breakout (working) groups. For each group, we describe its contributors, goals and key questions, key insights, and future research. The themes of the groups were derived from a pre-Seminar survey, which also led to a list of suggested readings for the topic of trust in conversational agents. The list is included in this report for references

Training a Chatbot with Microsoft LUIS: Effect of Intent Imbalance on Prediction Accuracy

The 25th International Conference on Intelligent User Interfaces Companion (IUI'20), Cagliari, Italy, 17-20 March 2020Microsoft LUIS is a natural language understanding service used to train Chatbots. Imbalance in the utterance training set may cause the LUIS model to predict the wrong intent for a user's query. We discuss this problem and the training recommendations from Microsoft to improve prediction accuracy with LUIS. We perform batch testing on three training sets created from two existing datasets to explore the effectiveness of these recommendations.Science Foundation IrelandMicrosoft Corporatio

BoTest: a Framework to Test the Quality of Conversational Agents Using Divergent Input Examples

ACM IUI (Intelligent User Interfaces), Tokyo, Japan, 07-11 March 2018Quality of conversational agents is important as users have high expectations. Consequently, poor interactions may lead to the user abandoning the system. In this paper, we propose a framework to test the quality of conversational agents. Our solution transforms working input that the conversational agent accurately recognises to generate divergent input examples that introduce complexity and stress the agent. As the divergent inputs are based on known utterances for which we have the 'normal' outputs, we can assess how robust the conversational agent is to variations in the input. To demonstrate our framework we built ChitChatBot, a simple conversational agent capable of making casual conversation.Science Foundation IrelandLer

Assessing the robustness of conversational agens using paraphrases

Assessing a conversational agent’s understanding capabilities is critical, as poor user interactions could seal the agent’s fate at the very beginning of its lifecycle with users abandoning the system. In this paper we explore the use of paraphrases as a testing tool for conversational agents. Paraphrases, which are different ways of expressing the same intent, are generated based on known working input by performing lexical substitutions. As the expected outcome for this newly generated data is known, we can use it to assess the agent’s robustness to language variation and detect potential understanding weaknesses. As demonstrated by a case study, we obtain encouraging results as it appears that this approach can help anticipate potential understanding shortcomings and that these shortcomings can be addressed by the generated paraphrases

Definition, conceptualisation and measurement of trust

This report documents the program and the outcomes of Dagstuhl Seminar 21381 "Conversational Agent as Trustworthy Autonomous System (Trust-CA)". First, we present the abstracts of the talks delivered by the Seminar’s attendees. Then we report on the origin and process of our six breakout (working) groups. For each group, we describe its contributors, goals and key questions, key insights, and future research. The themes of the groups were derived from a pre-Seminar survey, which also led to a list of suggested readings for the topic of trust in conversational agents. The list is included in this report for references