Goal-oriented conversational agents – a proposed approach for practical domains
Journal Title: Revista Romana de Interactiune Om-Calculator - Year 2018, Vol 11, Issue 4
Abstract
Conversational agents are nowadays desired in many fields, mainly for task automation, but also for entertaining, therapy or other purposes. This report will introduce the main Dialogue Systems categories, along with some of the most successful frameworks and algorithms, reported as state-of-the-art over the last few years, with a focus on Goal Oriented (GO) chatbots. Due to the domain specificity, large accomplishments are the most difficult to attain, conversational agents being able, in general, to encompass some of the most problematic natural language processing tasks such as intent classification, information extraction, text generation. Similar to other subfields of artificial intelligence and machine learning, in this field we find the same vital role of large quantities of annotated data. Some of the research discussed here with significant input towards the industry tries to maximize knowledge gain over decreasing the number of annotated instances to be fed to the system as training data. I further carry forth our experiments on a telecom dataset, along with results, conclusions and future work.
Authors and Affiliations
Stefania Budulan
Keywords
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