Evaluation of Utterances based on Causal Knowledge Retrieved from Blogs

Abstract

In this paper, we describe the effectiveness of utterance generation using causal knowledge for a dialogue system. Recently, there has been a variety of research on non-taskoriented dialogue systems; however, an effective approach has not yet been developed. One of the most important reasons for this is that non-task-oriented dialogue systems lack common sense knowledge, which is not in their databases. As the first step towards solving this problem, we concentrated on causal knowledge containing reasons and effects, which can provide unwritten meanings for utterance understanding and generating modules. In this paper we investigated how an utterance generated with knowledge related to user input can improve an existing conversational system. Experiment results show that utterance generation using causal knowledge can improve a conversational system.

Conversational Response Re-ranking Based on Event Causality and Role Factored Tensor Event Embedding

“…Event causality relations have been used in why-question answering systems to focus on causalities between questions and answers (Oh et al, 2013(Oh et al, , 2016(Oh et al, , 2017. It is also reported that a conversational model using event causality relations can generate diverse and coherent responses (Fujita et al, 2011). However, the relation between dialogue continuity and the coherency of system responses is still an underlying problem.…”

Relationship Between Coherence of Sequential Events and Dialogue Continuity in Conversational Response

“…Neural Conversation Model (NCM) (Vinyals and Le 2015) (Mei et al 2017) (Oh et al 2013;Jansen et al 2014;Bogdanova and Foster 2016;Ohmura and Eskenazi 2018) 2 (Shibata and Kurohashi 2011;Shibata et al 2014) (Oh et al 2013(Oh et al , 2016(Oh et al , 2017) (Fujita et al 2011; Vol. 28 No.…”