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Learning to Respond with Deep Neural Networks for Retrieval-Based Human-Computer Conversation System
2016
Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR '16
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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
2017
Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17
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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
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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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
2020
International Journal of Software Engineering and Its Applications
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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]
2018
arXiv
pre-print
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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
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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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
2020
International Journal for Research in Applied Science and Engineering Technology
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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]
2017
arXiv
pre-print
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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]
2016
arXiv
pre-print
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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
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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
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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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
2021
International Journal of Advanced Computer Science and Applications
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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
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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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
2020
International Journal of Emerging Trends in Engineering Research
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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]
2020
arXiv
pre-print
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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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