Paper Info
Title
Interpreting Human Responses in Dialogue Systems using Fuzzy Semantic Similarity Measures
Abstract
Dialogue systems are automated systems that interact with humans using natural language. Much work has been done on dialogue management and learning using a range of computational intelligence based approaches, however the complexity of human dialogue in different contexts still presents many challenges. The key impact of work presented in this paper is to use fuzzy semantic similarity measures embedded within a dialogue system to allow a machine to semantically comprehend human utterances in a given context and thus communicate more effectively with a human in a specific domain using natural language. To achieve this, perception based words should be understood by a machine in context of the dialogue. In this work, a simple question and answer dialogue system is implemented for a café customer satisfaction feedback survey. Both fuzzy and crisp semantic similarity measures are used within the dialogue engine to assess the accuracy and robustness of rule firing. Results from a 32 participant study, show that the fuzzy measure improves rule matching within the dialogue system by 21.88% compared with the crisp measure known as STASIS, thus providing a more natural and fluid dialogue exchange.
Year
DOI
Venue
2020
10.1109/FUZZ48607.2020.9177605
2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Keywords
DocType
ISSN
dialogue systems,conversational agents,fuzzy semantic similarity measures,fuzzy natural language
Conference
1544-5615
ISBN
Citations 
PageRank 
978-1-7281-6933-0
0
0.34
References 
Authors
11
4
Name
Order
Citations
PageRank
Naeemeh Adel100.34
Keeley A. Crockett214123.42
David Chandran332.46
João Paulo Carvalho411017.52