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Learning to Respond with Deep Neural Networks for Retrieval-Based Human-Computer Conversation System

Rui Yan, Yiping Song, Hua Wu
2016 Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR '16  
In this paper, we propose a retrieval-based conversation system with the deep learning-torespond schema through a deep neural network framework driven by web data.  ...  The challenges lie in how to respond so as to maintain a relevant and continuous conversation with humans.  ...  To sum up, our contributions are mainly as follows: • We propose a "deep learning-to-respond" schema for auto-matic human-computer conversation systems with deep neural networks (DNNs).  ... 
doi:10.1145/2911451.2911542 dblp:conf/sigir/YanSW16 fatcat:fnyvyxodczc6nczlvh5uzughja

Joint Learning of Response Ranking and Next Utterance Suggestion in Human-Computer Conversation System

Rui Yan, Dongyan Zhao, Weinan E.
2017 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17  
Owing to the diversity of Web resources, a retrieval-based conversation system will come up with at least some results from the immense repository for any user inputs.  ...  Given a human issued message, i.e., query, a traditional conversation system would provide a response after adequate training and learning of how to respond.  ...  ACKNOWLEDGMENTS We thank the reviewers for their insightful comments.  ... 
doi:10.1145/3077136.3080843 dblp:conf/sigir/YanZE17 fatcat:fydhrqqp35bpfamnol24il7ztm

"Chitty-Chitty-Chat Bot": Deep Learning for Conversational AI

Rui Yan
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
Along with the Web 2.0, the massive data available greatly facilitate data-driven methods such as deep learning for human-computer conversations.  ...  To build a conversational system with moderate intelligence is challenging, and requires abundant dialogue data and interdisciplinary techniques.  ...  Acknowledgments We would like to thank the anonymous reviewers for their constructive comments.  ... 
doi:10.24963/ijcai.2018/778 dblp:conf/ijcai/Yan18 fatcat:s36y6mwhf5gapg3ila7nuh2fo4

Chatbot Analytics Based on Question Answering System and Deep Learning: Case Study for Movie Smart Automatic Answering

Jugal Shah, Computer Science Department, Lakehead University, Ontario, Canada, Sabah Mohammed*, Computer Science Department, Lakehead University, Ontario, Canada
2020 International Journal of Software Engineering and Its Applications  
Question Answer (QA) systems are established to retrieves accurate and concise answers to human queries posted in natural language.  ...  In this paper, research is conducted on build a smart chatbots based QA system that employs a deep learning model.  ...  Acknowledgements I want to thank Dr. Sabah Mohammed for his support and supervision throughout this research project.  ... 
doi:10.21742/ijseia.2020.14.1.02 fatcat:tan3pq52xzbnvnoqafpidch6wa

A Survey on Dialogue Systems: Recent Advances and New Frontiers [article]

Hongshen Chen, Xiaorui Liu, Dawei Yin, Jiliang Tang
2018 arXiv   pre-print
For dialogue systems, deep learning can leverage a massive amount of data to learn meaningful feature representations and response generation strategies, while requiring a minimum amount of hand-crafting  ...  Recent advances on dialogue systems are overwhelmingly contributed by deep learning techniques, which have been employed to enhance a wide range of big data applications such as computer vision, natural  ...  to build data-driven, open-domain conversation systems between humans and computers.  ... 
arXiv:1711.01731v3 fatcat:6wuovcynqbhlzmuorchn4mn6ma

Smarter Response with Proactive Suggestion: A New Generative Neural Conversation Paradigm

Rui Yan, Dongyan Zhao
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
In this paper, we propose a new paradigm for neural generative conversations: smarter response with a suggestion is provided given the query.  ...  Thanks to the prosperity of Web 2.0, a large volume of conversational data become available to establish human-computer conversational systems.  ...  Acknowledgments We would like to thank the anonymous reviewers for their constructive comments.  ... 
doi:10.24963/ijcai.2018/629 dblp:conf/ijcai/YanZ18 fatcat:f3owyden5bf5bmbllhyp4o3asm

Chatbots & Its Techniques using AI: A Review

Santosh Maher
2020 International Journal for Research in Applied Science and Engineering Technology  
The aim of the chatbot framework for machine learn-ing and artificial intelligence is to simulate a human conver-sation, maybe through text or speech.  ...  This paper discusses other applications that may be useful for chatbots, such as a computer conversa-tion system, virtual agent, dialogue system, retrieval of infor-mation, industry, telecommunications  ...  Deep learning and neural networks are gaining prominence in the field of NLP, with hidden states between input and output and robust networking to produce the best performance. [22] . A.  ... 
doi:10.22214/ijraset.2020.32537 fatcat:ohtxwh77lfbivefp24qgssijbu

Neural Matching Models for Question Retrieval and Next Question Prediction in Conversation [article]

Liu Yang, Hamed Zamani, Yongfeng Zhang, Jiafeng Guo, W. Bruce Croft
2017 arXiv   pre-print
Neural matching models, which adopt deep neural networks to learn sequence representations and matching scores, have attracted immense research interests of information retrieval and natural language processing  ...  Our evaluations investigate the potential of neural matching models with representation learning for question retrieval and next question prediction in conversations.  ...  ACKNOWLEDGMENTS is work was supported in part by the Center for Intelligent Information Retrieval, in part by NSF IIS-1160894, and in part by NSF grant #IIS-1419693.  ... 
arXiv:1707.05409v1 fatcat:szimicqsijh2zb6lkizgi5zbxa

Emulating Human Conversations using Convolutional Neural Network-based IR [article]

Abhay Prakash, Chris Brockett, Puneet Agrawal
2016 arXiv   pre-print
In this paper, we introduce a model that employs Information Retrieval by utilizing convolutional deep structured semantic neural network-based features in the ranker to present human-like responses in  ...  To design a system that is capable of emulating human-like interactions, a conversational layer that can serve as a fabric for chat-like interaction with the agent is needed.  ...  In this paper, we present a deep neural network-based approach to implementing a conversational agent that will engage with users in a friendly, engaging, conversational fashion, drawing on a database  ... 
arXiv:1606.07056v1 fatcat:ul4zaxafmvbzbbsamz6p4rnvfa

Deep Learning for Conversational Agent via Context Question Answering Model

2020 International Journal of Advanced Trends in Computer Science and Engineering  
The Black-box Testing findings show that the average time taken for Deep Learning to produce a response is approximately two seconds.  ...  As a consequence, this study seeks to analyze and improve the current business platform by providing the ability to offer immediate feedback by integrating Deep Learning to the conversational agent.  ...  Conversational agent, or very often address as chatbot, refers to the attempts of the computer system to imitate human-machine communications.  ... 
doi:10.30534/ijatcse/2020/62952020 fatcat:ze6cng5wgzcknlfr5ivcugwu44

An Embodied Conversational Agent using Retrieval-Based Model and Deep Learning

This study is reliant on the Artificial Intelligence (AI) to offer natural language processing (NLP) by presenting retrieval-based model and Deep Learning to enable the conversation agent to make smarter  ...  of a conversational agent via deep learning, comparable to communicating with the competent customer service consultant.  ...  An Embodied Conversational Agent using Retrieval-Based Model and Deep Learning Pui Huang Leong, Ong Sing Goh, Yogan Jaya Kumar An Embodied Conversational Agent using Retrieval-Based Model and Deep Learning  ... 
doi:10.35940/ijitee.l3650.1081219 fatcat:noledaj2zbbebmwzxmerwg4egq

Robot Chat System (Chatbot) to Help Users "Homelab" based in Deep Learning

Aji Naufal Aqil, Burhanuddin Dirgantara, Istikmal -, Umar Ali Ahmad, Reza Rendian Septiawan, Alex Lukmanto Suherman
2021 International Journal of Advanced Computer Science and Applications  
In Homelab, besides the question and answer feature, a virtual conversation agent (chatbot) based on deep learning with a retrieval model that uses multilayer perceptron and a special text dataset for  ...  The system made has an accuracy rate of 96.43 percent with an average processing time of 0.3 seconds to get a response.  ...  A model system combined with deep learning techniques to provide more accurate responses [15] . Unlike retrieval-based model, a later one does not depend on a fixed response repository.  ... 
doi:10.14569/ijacsa.2021.0120870 fatcat:z6xxnqjdtza4tkfe7m4gkcmhaa

Chatbots Employing Deep Learning for Big Data

So involving deep learning amongst these models can overcome this lack and can fill up the paucity with deep neural networks.  ...  With the evolution of artificial intelligence to deep learning, the age of perspicacious machines has pioneered that can even mimic as a human.  ...  These are powerful than feedforward deep neural networks and acts as a general computer that can help to create and process memories with abundant input pattern sequences.  ... 
doi:10.35940/ijitee.i8017.0981119 fatcat:eszidvr2gndafbomcxvkhnhyiu

Chatterbot implementation using Transfer Learning and LSTM Encoder-Decoder Architecture

Kolla Bhanu Prakash
2020 International Journal of Emerging Trends in Engineering Research  
The goal of this project is to develop a chatbot using deep learning models.  ...  For that we have used a movie dialog corpus of 220,579 conversation exchanges among which about 50,000 conversational exchanges are only used as a training corpus to our model since training on more conversation  ...  Built to convincingly mimic how a human being will haves conversation with partner, chatbot systems usually testing and tuning in continuous way, and many in development remain unsuccessful effectively  ... 
doi:10.30534/ijeter/2020/35852020 fatcat:obt76fz54banfoxeh5vh2wmv4u

Challenges in Building Intelligent Open-domain Dialog Systems [article]

Minlie Huang, Xiaoyan Zhu, Jianfeng Gao
2020 arXiv   pre-print
Unlike traditional task-oriented bots, an open-domain dialog system aims to establish long-term connections with users by satisfying the human need for communication, affection, and social belonging.  ...  There is a resurgent interest in developing intelligent open-domain dialog systems due to the availability of large amounts of conversational data and the recent progress on neural approaches to conversational  ...  In Section 2, we survey three typical approaches to building neural-based open-domain dialog systems, namely, retrieval-based, generation-based, and hybrid methods.  ... 
arXiv:1905.05709v3 fatcat:vdibhr4sobgufgcab2cyfyg7wy
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