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Non-goal oriented dialogue agents: state of the art, dataset, and evaluation

Non-goal oriented dialogue agents: state of the art, dataset, and evaluation Dialogue agent, a derivative of intelligent agent in the field of computational linguistics, is a computer program that is capable of generating responses and performing conversation in natural language. The field of computational linguistics is flourishing due to the intensive growth of dialogue agents; the most potential one is providing voice controlled smart personal assistant service for handsets and homes. The agents are usable, accessible but perform task-related short conversations. Non-goal-oriented dialogue agents are designed to imitate extended human–human conversations, also called as chit-chat, to provide the consumer with a satisfactory experience on the conversation quality. The design of such agents is primarily defined by a language model, unlike goal-oriented dialogue agents that employees slot based or ontology-based frameworks, hence most of the methods are data-driven. This paper surveys the current state of the art of non-goal-oriented dialogue systems specifically data-driven methods, the most prevalent being deep learning. This paper aims at (a) providing an insight of recent methods and architectures proposed for building context and modeling response along with a comprehensive review of the state of the art (b) examine the type of data set and evaluation methods available (c) present the challenges and limitation that the recent models, dataset and evaluation methods constitute. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence Review Springer Journals

Non-goal oriented dialogue agents: state of the art, dataset, and evaluation

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References (91)

Publisher
Springer Journals
Copyright
Copyright © Springer Nature B.V. 2020
ISSN
0269-2821
eISSN
1573-7462
DOI
10.1007/s10462-020-09848-z
Publisher site
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Abstract

Dialogue agent, a derivative of intelligent agent in the field of computational linguistics, is a computer program that is capable of generating responses and performing conversation in natural language. The field of computational linguistics is flourishing due to the intensive growth of dialogue agents; the most potential one is providing voice controlled smart personal assistant service for handsets and homes. The agents are usable, accessible but perform task-related short conversations. Non-goal-oriented dialogue agents are designed to imitate extended human–human conversations, also called as chit-chat, to provide the consumer with a satisfactory experience on the conversation quality. The design of such agents is primarily defined by a language model, unlike goal-oriented dialogue agents that employees slot based or ontology-based frameworks, hence most of the methods are data-driven. This paper surveys the current state of the art of non-goal-oriented dialogue systems specifically data-driven methods, the most prevalent being deep learning. This paper aims at (a) providing an insight of recent methods and architectures proposed for building context and modeling response along with a comprehensive review of the state of the art (b) examine the type of data set and evaluation methods available (c) present the challenges and limitation that the recent models, dataset and evaluation methods constitute.

Journal

Artificial Intelligence Review – Springer Journals

Published: Jun 5, 2020

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