Abstract
Amazon’s Alexa, Apple’s Siri, Google Assistant and Microsoft’s Cortana, clearly illustrate the impressive research work and potentials to be explored in the field of conversational agents. Conversational agent, chatter-bot or chatbot is a program expected to converse with near-human intelligence. Chatbots are designed to be used either as task-oriented ones or simply open-ended dialogue generator. Many approaches have been proposed in this field which ranges from earlier versions of hard-coded response generator to the advanced development techniques in Artificial Intelligence. In a broader sense, these can be categorized as rule-based and neural network based. While rule-based relies on predefined templates and responses, a neural network based relies on deep learning models. Rule-based are preferable for simpler task-oriented conversations. Open-domain conversational modeling is a more challenging area and uses mostly neural network-based approaches. This paper begins with an introduction of chatbots, followed by in-depth discussion on various classical or rule-based and neural-network-based approaches. The evaluation metrics employed for chatbots are mentioned. The paper concludes with a table consisting of recent research done in the field. It covers all the latest and significant publications in the field, the evaluation metrics employed, the corpus which is used as well as the possible areas of enhancement that exist in the proposed techniques.
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Turing BAM. Computing machinery and intelligence. In: Parsing the turing test. Springer, Dordrecht; 2009. p. 23–65.
Serban IV et al. A deep reinforcement learning chatbot. arXiv preprint arXiv: 1709. 02349v [cs . CL] 2017; 1–40.
Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems; 2012. p. 1097–105.
Havrylov S, Titov I. Emergence of language with multi-agent games: learning to communicate with sequences of symbols. In: Advances in neural information processing systems; 2017, p. 2149–59.
Van Merri B, Fellow CS. Learning phrase representations using RNN encoder—decoder for statistical machine translation. arXiv preprint arXiv: 1724–1734. 2014.
Li X, Mou L, Yan R, Zhang M. StalemateBreaker : a proactive content-introducing approach to automatic human-computer conversation. arXiv preprint arXiv:1604.04358. 1:2845–2851.
Sutskever I, Vinyals O, Le QV. Sequence to sequence learning with neural networks. In: Advances in neural information processing systems. p. 3104–12.
Song Y, Yan R, Li X, Zhao D, Zhang M. Two are better than one : an ensemble of retrieval- and generation-based dialog systems. arXiv preprint arXiv:1610.07149. 2016 ; 1:1–11.
Clarizia F, Colace F, Lombardi M. Chatbot : an education support system for student, vol. 1. Berlin: Springer; 2018.
Colace F, De Santo M, Pascale F, Lemma S, Lombardi M. BotWheels: a petri net based chatbot for recommending tires. In: Data; 2017. p. 350–8.
Edwards BI, Muniru IO, Cheok AD. Robots to the rescue: a review of studies on differential medical diagnosis employing ontology-based chat bot technology. Preprints. 2016. https://doi.org/10.20944/preprints201612.0027.v1.
Casillo M, Clarizia F, Aniello GD, De Santo M, Lombardi M, Santaniello D. CHAT-Bot: a Cultural Heritage Aware Teller-Bot for supporting touristic experiences. Pattern Recognit Lett. 2020;131:234–43.
Witbrock M. Conversational crowd based and context aware knowledge acquisition chat bot. 2016; 239–252.
Weizenbaum J. ELIZA—a computer program for the study of natural language communication between man and machine. Commun ACM. 1966;9(1):36–45.
Ahmad S. Tutorial on natural language processing. Artif Intell. 2007;810:161.
“https://www.jabberwacky.com/j2about.” [Online]. Available: https://www.jabberwacky.com/j2about. Accessed 21 July 2019.
Bradeško L, Mladenić D. A survey of chabot systems through a loebner prize competition. Res Net. 2012;2:1–4.
Hutchens JL, Alder MD. Introducing MegaHAL II I ! II. Computer (Long. Beach. Calif). 2000;1998:271–4.
Noy NF, McGuinness DL. Ontology development 101: a guide to creating your first ontology. Stanford: Stanford Knowl Syst Lab; 2001. p. 25.
Lenat DBCYC. A large-scale investment in knowledge infrastructure. Commun ACM. 1995;38(11):33–8.
Zubaide HAl, Issa AA. OntBot: ontology based ChatBot. 2011 4th Int Symp Innov Inf Commun Technol. ISIICT’2011. p 7–12,
BrunoG, De Aguiar RV, Barbosa GDO, Botelho WT, Pimentel E. A RTIFICIAL I NTELLIGENCE M ARKUP L ANGUAGE : A B RIEF T UTORIAL.
Wallace R. The elements of AIML style. Alice AI Foundation 139; 2003.
Wallace RS. The anatomy of ALICE. In: Parsing the turing test. Dordrecht: Springer; 2009. p. 181–210.
Wilcox B, Wilcox S. Suzette, the most human computer. Agent’s Processing, Cognition. 2010. https://www.chatbots.org/images/uploads/research_papers/9491.pdf.
Razak LT. Extension and prerequisite : an algorithm to enable relations between responses in chatbot technology abbas saliimi lokman and jasni mohamad zain faculty of computer systems and software engineering. J Comput Sci. 2010;6(10):1212–8.
Hochreiter S. The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int J Uncertainty Fuzziness Knowl Based Syst. 1998;6(2):107–16.
Cascade-correlation R, Chunking NS. Long short term memory. Neural Comput. 1997;9(8):1–32.
Greff K, Srivastava RK, Koutnik J, Steunebrink BR, Schmidhuber J. LSTM: a search space odyssey. IEEE Trans Neural Networks Learn Syst. 2017;28(10):2222–32.
Vinyals O, Le Q. A neural conversational model. 2015. arXiv:1506.05869.
Papineni K, Roukos S, Ward T, Zhu W. BLEU: a method for automatic evaluation of machine translation. 2002; 311–318.
Manning C, Schutze H. Foundations of statistical natural language processing. MIT press; 1999.
Galley M, Auli M, Brockett C, Ji Y, Mitchell M, Gao J. A neural network approach to context-sensitive generation of conversational responses. arXiv preprint arXiv:1506.06714. 2015.
Peng B, Zweig G. An attentional neural conversation model with improved specificity. arXiv preprint arXiv:1606.01292. 2016.
Zhao T, Lu A, Lee K, Eskenazi M. Generative encoder-decoder models for task-oriented spoken dialog systems with chatting capability. arXiv preprint arXiv:1706.08476. 2017; 27–36.
Gabriel R et al. On evaluating and comparing conversational agents. Nips. 2017; 1–10.
Shang L, Lu Z, Li H. Neural responding machine for short-text conversation. 2015; 1577–1586.
Serban IV, Sordoni A, Bengio Y, Courville A, Pineau J. Building end-to-end dialogue systems using generative hierarchical neural network models. 2015,
Gao J. A diversity-promoting objective function for neural conversation models. 2015.
Zweig V. Attention with Intention for a Neural Network Conversation Model. 2015; 1–7.
Li J, Galley M, Brockett C, Spithourakis GP, Gao J, Dolan B. A persona-based neural conversation model. 2016.
Serban IV, Sordoni A, Lowe R, Charlin L, Pineau J. A hierarchical latent variable encoder-decoder model for generating dialogues. 2016.
Li J, Monroe W, Ritter A, Galley M, Gao J, Jurafsky D. Deep reinforcement learning for dialogue generation. 2016.
Pierre JM, Butler M, Portnoff J, Aguilar L. Neural discourse modelling of conversations. 2016; 5(6):1–8.
Xiong K, Cui A, Zhang Z, Li M. Neural contextual conversation learning with labeled question-answering pairs. 2014, 2016.
Mou L, Song Y, Yan R, Li G, Zhang L, Jin Z. Sequence to backward and forward sequences: a content-introducing approach to generative short-text conversation. 2016.
Li J, Monroe W, Shi T, Jean S, Ritter A, Jurafsky D. Adversarial learning for neural dialogue generation. 2017.
Ghazvininejad M et al. A knowledge-grounded neural conversation model. 2017.
Williams JD, Asadi K, Zweig G. Hybrid code networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning. 2017.
Zhao T, Zhao R, Eskenazi M. Learning discourse-level diversity for neural dialog models using conditional variational autoencoders. 2017.
Young T, Cambria E, Chaturvedi I, Huang M, Zhou H, Biswas S. Augmenting end-to-end dialog systems with commonsense knowledge. 2017.
Liu B, Tur G, Hakkani-Tur D, Shah P, Heck L. End-to-end optimization of task-oriented dialogue model with deep reinforcement learning. 2017; 1–6.
Zhang S, Dinan E, Urbanek J, Szlam A, Kiela D, Weston J. Personalizing dialogue agents: i have a dog, do you have pets too?. 2018.
Kim G. A hierarchical latent structure for variational conversation modeling. 2018.
Zhao T, Lee K, Eskenazi M. Unsupervised discrete sentence representation learning for interpretable neural dialog generation. 2018.
Gu X, Cho K, Ha J, Kim S. DialogWAE : multimodal response generation with conditional wasserstein auto-encoder. 2018; 1–10.
Olabiyi OO, Salimov A, Mueller ET. Multi-turn dialogue response generation in an adversarial learning framework. 2018.
Zhang Y, Gan Z, Brockett C. Generating informative and diverse conversational responses via adversarial information maximization. Nips. 2018.
Devlin J, Chang M-W, Lee K, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. 2018.
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This article is part of the topical collection “Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications” guest edited by Bhanu Prakash K N and M. Shivakumar”.
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Agarwal, R., Wadhwa, M. Review of State-of-the-Art Design Techniques for Chatbots. SN COMPUT. SCI. 1, 246 (2020). https://doi.org/10.1007/s42979-020-00255-3
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DOI: https://doi.org/10.1007/s42979-020-00255-3