User Interest Modelling in Argumentative Dialogue Systems
Most systems helping to provide structured information and support opinion building, discuss with users without considering their individual interest. The scarce existing research on user interest in dialogue systems depends on explicit user feedback. Such systems require user responses that are not content-related and thus, tend to disturb the dialogue flow. In this paper, we present a novel model for implicitly estimating user interest during argumentative dialogues based on semantically clustered data. Therefore, an online user study was conducted to acquire training data which was used to train a binary neural network classifier in order to predict whether or not users are still interested in the content of the ongoing dialogue. We achieved a classification accuracy of 74.9% and furthermore investigated with different Artificial Neural Networks (ANN) which new argument would fit the user interest best.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Introduction method for argumentative dialogue using paired question-answering interchange about personality
To provide a better discussion experience in current argumentative dialogue systems, it is necessary for the user to feel motivated to participate, even if the system already responds appropriately. In this paper, we pro…
Decision MakingQuestion AnsweringEvaluation of Argument Search Approaches in the Context of Argumentative Dialogue Systems
We present an approach to evaluate argument search techniques in view of their use in argumentative dialogue systems by assessing quality aspects of the retrieved arguments. To this end, we introduce a dialogue system th…
Dave the debater: a retrieval-based and generative argumentative dialogue agent
In this paper, we explore the problem of developing an argumentative dialogue agent that can be able to discuss with human users on controversial topics. We describe two systems that use retrieval-based and generative mo…
Argument MiningRetrievalStance DetectionTowards Modelling Self-imposed Filter Bubbles in Argumentative Dialogue Systems
To build a well-founded opinion it is natural for humans to gather and exchange new arguments. Especially when being confronted with an overwhelming amount of information, people tend to focus on only the part of the ava…
From Argument Search to Argumentative Dialogue: A Topic-independent Approach to Argument Acquisition for Dialogue Systems
Despite the remarkable progress in the field of computational argumentation, dialogue systems concerned with argumentative tasks often rely on structured knowledge about arguments and their relations. Since the manual ac…
Relation Classification