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.

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Fig. 3

Source: Ji et al. (2014)



Convolutional deep structured semantic model


Convolution neural network


Convolution neural network language model


Continuous bag of words


Generative adversarial network


Gated recurrent unit


Information retrieval


Mean average precision


Markov decision processes


Maximum likelihood estimation


Mean reciprocal rank


Machine translation


Natural language processing


Natural language understanding


Natural language generation


Neural network based language model


Partial observable Markov decision process


Probabilistic neural network




Recurrent neural network


Recurrent neural network based language models


Statistical machine translation


Support vector machine


Latent Dirichlet allocation


Long short term memory


Long short term memory-based language model


Word error rate


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Mehndiratta, A., Asawa, K. Non-goal oriented dialogue agents: state of the art, dataset, and evaluation. Artif Intell Rev 54, 329–357 (2021).

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  • Natural language processing
  • Dialogue management systems
  • Language modeling
  • Machine learning
  • Deep learning
  • Dialogue agent