Backpropagation & Chatbots 2019


See also:

AI Algorithms Meta Guide


CAISY: Chatbot using Artificial Intelligence and Sequential Model with YAML Dataset
P JM, S Kashyap, PR Nargund – 2019 – papers.ssrn.com
… Between the 80s – 90s numerous chatbots were developed – some predominant ones are ALICE … The chatbot was launched on different platforms like AOL, IM and MSN Messenger … With conventional Backpropagation Through Time” (BPTT, eg, Williams and Zipser 1992, Werbos …

Developing Dialog Manager in Chatbots via Hybrid Deep Learning Architectures
B Ali, V Ravi – Intelligent Data Engineering and Analytics, 2020 – Springer
… All these Chatbots, independent of the domain, have a common behavior, ie, task oriented … Cross-Entropy Loss function is used to learn the trainable parameters during backpropagation … three hybrid deep learning architectures for the dialog manager to be used in Chatbot …

Applied natural language processing inspired by fundamental mathematics and physics
RR Dangovski – 2020 – dspace.mit.edu
… text summarization, question answering, and building chatbots/dialog systems. As standard RNNs suffer from exploding/vanishing gradient problems, alterna … with Backpropagation Through Time (BPTT) (Rumelhart et al., 1986) …

A Personalised Dialogue System based on Person Identification
L Frank – 2020 – isl.anthropomatik.kit.edu
… () = (( The recurrent network is trained with the backpropagation through time algorithm … [2] an overview of the di erent dialogue systems and the … According to their di erentiation this work is a non-task-oriented dialogue system with a way to partially incorporate memory …

End-to-end memory networks: a survey
R Jafari, S Razvarz, A Gegov – Science and Information Conference, 2020 – Springer
… with multiple hops in order to generate \(\tilde{a}\). This permits backpropagation of the … 49, 245–250 (2015)CrossRefGoogle Scholar. 3. Quarteroni, S.: A chatbot-based interactive question … 1160–1179 (2013)CrossRefGoogle Scholar. 5. Shawar, BA, Atwell, E.: Chatbots: are they …

LSTM for Dialogue Breakdown Detection: Exploration of Different Model Types and Word Embeddings
M Hendriksen, A Leeuwenberg… – … Workshop on Chatbots …, 2019 – workshop.colips.org
… in- stance, [22] offer a similar technique for assessment of chatbot responses in … RMSProp)[19] which is the extension of Re- silient Backpropagation (Rprop) learning … Overview of dialogue breakdown detection challenge 3. Proceedings of Dialog System Technology Challenge 6 …

Deep reinforcement learning: a survey
H Wang, N Liu, Y Zhang, D Feng, F Huang, D Li… – Frontiers of Information …, 2020 – Springer
… has been widely used in various fields, such as end-to-end control, robotic control, recommendation systems, and natural language dialogue systems … estimate the uncertainty in a neural network: (a) each weight has a fixed value provided by classical backpropagation; (b) …

An AI-Based Chatbot Using Deep Learning
M SENTHILKUMAR… – … Systems: Advances in …, 2019 – books.google.com
… All the estimation and evaluation for predicting cost, and backpropagation is take care … Receding from the general use chatbots are given, a useful product can be … KEYWORDS • chatbot • deep learning • RNN • LSTM • BPTT • AdaGrad optimizer REFERENCES 1. Bahdanau, D …

Promoting diversity for end-to-end conversation response generation
YP Ruan, ZH Ling, Q Liu, JC Gu, X Zhu – arXiv preprint arXiv:1901.09444, 2019 – arxiv.org
… 2013) and non- task oriented chatbots … The Dialog System Technology Challenges (DSTC) in its seventh edition offers a track (Track 2) (Galley et al … To guarantee the feasibility of error backpropagation for model training, reparametrization (Kingma and Welling 2013) is …

Towards emotion-sensitive conversational user interfaces in healthcare applications
K Denecke, R May, Y Deng – Studies in health technology and …, 2019 – arbor.bfh.ch
… Figure 1. Compositional function of recursive neural networks and backpropagation of errors … Addressing challenges in healthy lifestyles: The Al-chatbot approach … 408-411 [4] F. Amato, S. Marrone, V. Moscato, G. Piantadosi, A. Picariello, C. Sansone: Chatbots Meet eHealth …

A Deep Multi-task Model for Dialogue Act Classification, Intent Detection and Slot Filling
M Firdaus, H Golchha, A Ekbal, P Bhattacharyya – Cognitive Computation, 2020 – Springer
… To create robust human/machine dialogue systems or chatbots, it is essential to understand the … realistic and natu- ral utterances spoken by the speakers in a human/machine dialogue system … LSTM/Bi-GRU layer), to enhance the flow of gradients during backpropagation, ie the …

A Compression-based BiLSTM for Treating Teenagers’ Depression Chatbot
J YIN – DEStech Transactions on Computer Science and …, 2019 – dpi-proceedings.com
… 1 ( ) nt jtj j t ot Lx – + = – = 1) Where L is the parameter of the layer trained by backpropagation. CNN-based Natural Language Processing … [2] Abdul-Kader SA, Woods J C. Survey on chatbot design techniques in speech … [3] Skjuve M, Brandtzæg P B. Chatbots as a new user …

Hybrid Supervised Reinforced Model for Dialogue Systems
C Miranda, Y Kessaci – arXiv preprint arXiv:2011.02243, 2020 – arxiv.org
… Our paper is structured as follows. In section 2, we give a brief introduction of reinforcement learning for chatbots by defining the two main components of Dialogue Management … 3 Chatbot environment … Backpropagation affects prediction steps only …

Constructing Interpretive Spatio-Temporal Features for Multi-Turn Responses Selection
J Lu, C Zhang, Z Xie, G Ling, TC Zhou… – Proceedings of the 57th …, 2019 – aclweb.org
… 2001. Overfitting in neural nets: Backpropagation, conju- gate gradient, and early stopping. pages 402–408 … 2019. Page 7. 50 Dialog system technology challenge 7. CoRR, abs/1901.03461 … 2018. Multi-turn response selection for chatbots with deep attention matching network …

Open Domain Conversational Chatbot
V Deshmukh, SJ Nirmala – International Conference on Information …, 2019 – Springer
… There is a huge range of chatbots identified based on the learning capacity and the … Training of the network is done using a generalized backpropagation algorithm and the error is … Bao, J., Chen, P., Zhou, M.: DocBot: an information retrieval approach for chatbot engines using …

CERG: Chinese Emotional Response Generator with Retrieval Method
Y Zhou, F Ren – Research, 2020 – spj.sciencemag.org
… system responds to the topics or instructions thrown by the user by simulating human beings [2]. Based on whether the dialogue system can achieve a specific goal, it can be divided into 2 types: task-oriented and non-task-oriented dialogue systems (or chatbot) [3]. Task …

Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
J Liu, Z Lin, S Padhy, D Tran… – Advances in …, 2020 – proceedings.neurips.cc
… For example, for a natural language understanding (NLU) model built for a domain-specific chatbot service (eg, weather inquiry), the user’s input utterance to the model can be of any topic, and the model needs to understand reliably and in real-time whether to abstain or to …

Exploiting persona information for diverse generation of conversational responses
H Song, WN Zhang, Y Cui, D Wang, T Liu – arXiv preprint arXiv …, 2019 – arxiv.org
… context in two main aspects: (i) it keeps unchanged throughout the conversation, and (ii) it is only the unilateral information (here is for the chatbot), while two … Finally, the Persona- CVAE model is trained with the sum of these losses and op- timized through backpropagation …

Integrating breakdown detection into dialogue systems to improve knowledge management: encoding temporal utterances with memory attention
S Lee, D Lee, D Hooshyar, J Jo, H Lim – Information Technology and …, 2020 – Springer
… [12], respectively, is an essential task for robust dialog systems to handle … the embeddings of all past system and user utterances in memory, not only training via backpropagation is easier … Finally, YI-100 is a collection of 100 dialogue sessions using a chat bot developed at the …

Question Generation with Adaptive Copying Neural Networks
X Lu – 2019 – curve.carleton.ca
… 23 2.14 An example of dialog system [7]. . . . 24 … people as if they were human. Medical chatbots will soon function as assistants … a random approximation of gradient descent and is often used with backpropagation to train neural networks …

Chatbots: History, technology, and applications
E Adamopoulou, L Moussiades – Machine Learning with Applications, 2020 – Elsevier
… Abstract. This literature review presents the History, Technology, and Applications of Natural Dialog Systems or simply chatbots. It aims to organize critical information that is a necessary background for further research activity in the field of chatbots …

Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning
MT Pilehvar… – Synthesis Lectures on …, 2020 – morganclaypool.com
… 2020 Conversational AI: Dialogue Systems, Conversational Agents, and Chatbots Michael McTear 2020 Natural Language Processing for Social Media, Third Edition Anna Atefeh Farzindar and Diana Inkpen 2020 Statistical …

Generating diverse conversation responses by creating and ranking multiple candidates
YP Ruan, ZH Ling, X Zhu, Q Liu, JC Gu – Computer Speech & Language, 2020 – Elsevier
… task oriented dialog systems (Young et al., 2013) and non-task oriented chatbots … to generate responses with substantial contents and thus make the chatbot more knowledgeable … To guarantee the feasibility of error backpropagation for model training, reparametrization (Kingma …

Development of customized conversational interfaces with Deep Learning techniques
P Cañas Castellanos – 2020 – e-archivo.uc3m.es
… is to show the whole process of the development of a spoken dialog system with Deep … However, it has been shown that using a chatbot for such functionality can multiply … Also named Backpropagation Rule, it consisted of updating the weights by distributing errors backwards …

Deep learning based chatbot models
R Csaky – arXiv preprint arXiv:1908.08835, 2019 – arxiv.org
… These chatbot programs are very similar in their core, namely that they all use hand … In this work, only deep learning methods applied to chatbots are discussed, since neural networks … step i. Training of these networks is done via the generalized backpropagation algorithm called …

Aging Memories Generate More Fluent Dialogue Responses with Memory Networks
OU Florez, E Mueller – arXiv preprint arXiv:1911.08522, 2019 – arxiv.org
… large amount of dialogue data recorded in human-human or human-chatbot interactions, there … in a realistic scenario: As the external memory of a dialogue system that infers … memory augmented neural networks by breaking co-adapting memories built during backpropagation …

Open-Domain Dialogue Generation: Presence, Limitation and Future Directions
H Yang, W Rong, Z Xiong – Proceedings of the 2019 7th International …, 2019 – dl.acm.org
… The application scenario of it, also chat-bot, is very extensive, from website customer … certainly increase the expression ability and expression diversity of the chatbot.(2) information … JS Denker, D. Henderson, RE Howard, WE Hubbard, and LD Jackel, “Backpropagation applied to …

Seq-DNC-seq: Context Aware Dialog Generation System Through External Memory
D Kang, M Lee – 2019 International Joint Conference on Neural …, 2019 – ieeexplore.ieee.org
… Index Terms—Context, chatbot, memory, DNC, seq2seq … LSTM decoders and the LSTMs in the DNC control functions are trained by using backpropagation through time … [5] A. Kamphaug, O.-C. Granmo, M. Goodwin, and VI Zadorozhny, “Towards open domain chatbots—a gru …

Recurrent neural models and related problems in natural language processing
S Zhang – 2019 – papyrus.bib.umontreal.ca
… The fourth article tackles the problem of the lack of personality in chatbots … iv Page 5. cessing, reading comprehension, dialogue system v Page 6. Contents Résumé . . . . … 12 2.2.2 Backpropagation Through Time (BPTT) …

Lecture 10: Recurrent Neural Networks
NL Zhang – Machine Learning – cse.ust.hk
… They are computed using Backpropagation Through Time (BPTT), which is an adaption of Backpropagation to the unrolled computational graph … RNN Architectures Seq2seq for ChatBot The generated sequence can also be a reply to the input sequence in dialogue systems …

Natural language understanding for dialogue systems using n-best lists
S Mansalis – MS thesis, 2019 – aueb.gr
… of robust speech recognition systems has attracted more and more the attention of dialogue system researchers both in research and industry … and non-task-oriented dialogue systems (also known as chatbots). Task-oriented dialogue … (Backpropagation through time) …

Humour-in-the-loop: Improvised Theatre with Interactive Machine Learning Systems
KW Mathewson – 2019 – era.library.ualberta.ca
… 186 B.8 Chatbot Competitions … 187 B.9 The Future of Chatbots … 1 Page 18. exceptional domain for experimentation toward improving dialogue generation systems. Improvised theatre is characterized by adaptive performers sponta …

Decoupling emergent strategies in task-oriented negotiation dialogue systems
? ????????? – 2020 – dspace.lib.ntua.gr
… Decoupling Emergent Strategies in Task-Oriented Negotiation Dialogue Systems ??????????? ??????? ??? ????????? ?. ???????? ?????????: ?????????? ?????????? ?? … 75 6 ????????? ????????? 77 6.1 Chatbots ? Open-Domain Conversational Agents …

Knowledge Augmented Dialogue Generation with Divergent Facts Selection
B Jiang, J Yang, C Yang, W Zhou, L Pang… – Knowledge-Based …, 2020 – Elsevier
… 4], which can be widely utilized in applications such as chatbots, personal assistants … complicated responses, which can significantly improve the performance of the dialogue system [5], [6 … Earlier works of dialogue systems are mainly concentrated on using the rules or template …

Rotational unit of memory: A novel representation unit for rnns with scalable applications
R Dangovski, L Jing, P Nakov, M Tatalovi?… – Transactions of the …, 2019 – MIT Press
… of-speech tagging and named entity recognition to neural machine translation, text summarization, question answering, and building chatbots/ dialog systems … 2016a; Zhang and Zhou, 2017) suggest that its updates can be learned efficiently with backpropagation through time …

STYLIZED NATURAL LANGUAGE GENERATION IN DIALOGUE SYSTEMS
K BOLSHAKOVA – dspace.vutbr.cz
… Chatbots have also been used for testing theories of psychological counseling [53 … Until recently Natural Language Generation component of a dialog system used primarily hand-coded … Backpropagation [41] is used for computing a gradient descent with respect to the weights of …

Pacgan: The power of two samples in generative adversarial networks
Z Lin, A Khetan, G Fanti, S Oh – IEEE Journal on Selected Areas …, 2020 – ieeexplore.ieee.org
… frame prediction [8], image super- resolution [9], and image-to-image translation [10]), as well as dialogue systems or chatbots—applications where … Backpropagation in such a network is dominated by the matrix-vector multiplication in each hidden layer, which has complexity …

A Survey of Knowledge-Enhanced Text Generation
W Yu, C Zhu, Z Li, Z Hu, Q Wang, H Ji… – arXiv preprint arXiv …, 2020 – arxiv.org
… text to include salient information; question answering (QA) generates textual answers to given questions; dialogue system supports chatbots to communicate … For example, in dialogue systems, conditioning on only the input text, a text generation system often produces …

Automated scoring of chatbot responses in conversational dialogue
SK Yuwono, B Wu, LF D’Haro – … Workshop on Spoken Dialogue System …, 2019 – Springer
… this paper, we have addressed the task of automated scoring of chatbot responses or … In: WOCHAT: workshop on chatbots and conversational agent technologiesGoogle Scholar. 3. Banchs … JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to …

Topic-aware chatbot using Recurrent Neural Networks and Nonnegative Matrix Factorization
Y Guo, N Haonian, Z Lin, N Liskij, H Lyu… – arXiv preprint arXiv …, 2019 – arxiv.org
… [XWW+17] introduced the concept of LDA- based topic attention in generative chatbots … TOPIC-AWARE CHATBOT USING RNN AND NMF … technique for numerically solving the above optimization problem for training the encoder-decoder is called backpropagation through time …

A Generative Dialogue System for Reminiscence Therapy
M Carós Roca – 2019 – upcommons.upc.edu
… architectures to be used to develop an intuitive, easy to use, and robust dialogue system for people at … Code is publicly available at GitHub2. We borrowed code from the Pytorch chatbot tutorial3 … However the origin of chatbots goes back to 1966 with the creation of ELIZA [37] by …

Self-Attentional Models Application in Task-Oriented Dialogue Generation Systems
M Saffar Mehrjardi – 2019 – era.library.ualberta.ca
… BOW Bag of Words BPTT Backpropagation Through Time CNN Convolutional Neural Network … engaged when they feel that they have become friends with the chatbot. Ama … chatbots, and customer-service chatbots. Deployment of task-oriented chat …

Intent Classification for Dialogue Utterances
J Schuurmans, F Frasincar – IEEE Intelligent Systems, 2019 – ieeexplore.ieee.org
… There is the Chatbot Corpus on Travel Scheduling, and the StackExchange Corpus on Ask … H. Glabska, and A. Wroblewska, “Multi- intent hierarchical natural language understanding for chatbots,” in Proc … PJ Werbos, “Backpropagation through time: What it does and how to do it …

A survey of natural language generation techniques with a focus on dialogue systems-past, present and future directions
S Santhanam, S Shaikh – arXiv preprint arXiv:1906.00500, 2019 – arxiv.org
… Keywords: deep learning, language generation, dialog systems … ments and rhetorical relations between segments to construct a text plan for their dialogue system … in tackling the important problems of vanishing and exploding gradients caused by backpropagation while training …

An attentive survey of attention models
S Chaudhari, G Polatkan, R Ramanath… – arXiv preprint arXiv …, 2019 – arxiv.org
… Answering, Sentiment Analysis, Part-of-Speech tagging, Constituency Parsing and Dialogue Systems … 4.2 Memory Networks Applications like question answering and chat bots require the ability to … the objective continuous and enabling end-to-end training via backpropagation …

Follow Alice into the Rabbit Hole: Giving Dialogue Agents Understanding of Human Level Attributes
AW Li, V Jiang, SY Feng, J Sprague, W Zhou… – arXiv preprint arXiv …, 2019 – arxiv.org
… Open-domain chatbots are more generic dialogue sys- tems … composite of elements of identity) as a possible solution at the word level, using backpropagation to align … on the target character language style retrieval task compared to the baseline open- domain chatbot models …

Towards automated emotional conversation generation with implicit and explicit affective strategy
X Gu, W Xu, S Li – Proceedings of the 2019 International Symposium on …, 2019 – dl.acm.org
… An emotional chatbot is created to converse with humans capable of understanding, regulating and … more fine-grained emotions interaction scenarios, eg, customer service chatbots and domestic … Stochastic backpropagation and approximate inference in deep generative models …

Variable-Length Chromosome for Optimizing the Structure of Recurrent Neural Network
MH Aliefa, S Suyanto – … Conference on Data Science and Its …, 2020 – ieeexplore.ieee.org
… Miller uses GA, a class of EA, to build neural network structures and uses backpropagation to search for weights [35] … [8] MH Toding Bunga and S. Suyanto, “Developing a Complete Dialogue System Using Long … [9] YW Chandra and S. Suyanto, “Indonesian Chatbot of University …

Learning to Align Question and Answer Utterances in Customer Service Conversation with Recurrent Pointer Networks
S He, K Liu, W An – Proceedings of the AAAI Conference on Artificial …, 2019 – aaai.org
… tion and answer utterances in a CS conversation, it is very useful for CS conversation analysis and building intelli- gent dialogue systems … for i-th utterance, which are represented by one- hot vectors for the convenience of calculation, especially the backpropagation of gradients …

Passive Diagnosis of Mental Health Disorders Incorporating an Empathic Dialogue System
F Delahunty, M Arcan, R Johansson – 2019 – thesiscommons.org
… 3Commonly known as chatbots … par- ticipants from the public domain who had a short conversation with our proposed empathic dialogue system … To evaluate Hypothesis 2, we randomly allocated recruited participants to have conversations with one of two dialogue systems …

Deep Reinforcement Learning for Text and Speech
U Kamath, J Liu, J Whitaker – Deep Learning for NLP and Speech …, 2019 – Springer
… current state. Sound familiar? This is similar to the notion of backpropagation. 13.2.4 Optimality. The goal of … on text. In particular, they have been very successful in building conversational agents and dialogue systems. In the next …

Static-Dynamically Attentive Variational Network for Dialogue Generation.
J Chang, R He, L Wang, R Wang, X Zhao… – Aust. J. Intell. Inf …, 2019 – ajiips.com.au
… of large conversation corpora on the Internet recently, building a data-driven chatbot has become … network: A new architecture for multi-turn response selection in retrieval-based chatbots … R., Lawrence, S., Giles, CL: Overfitting in neural nets: Backpropagation, conjugate gradient …

A survey on construction and enhancement methods in service chatbots design
Z Peng, X Ma – CCF Transactions on Pervasive Computing and …, 2019 – Springer
… 2, modular task-oriented dialog system mainly consists of three components (Shum et al … Microsoft LUIS (2018), IBM Watson Assistant (2018) and Dialogflow (2018) that provide easy-to-use SLU, DM and NLG services to help chatbot designers build service chatbots …

Conversational agent with common-sense: Responding to nonsensical statements
AP Konar – 2020 – era.library.ualberta.ca
… sensical, then further actions are taken. 3. How can a chatbot learn common sense and generate a re- sponse based on a nonsensical statement … chatbots as well as in AI systems for physics … However, Pascanu shows in [36] that during backpropagation, the gradi …

ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT
C Huang, A Trabelsi, OR Zaïane – arXiv preprint arXiv:1904.00132, 2019 – arxiv.org
… In such cases, a user is conversing with an automatic chatbot. Empowering the chat- bot with the ability to detect the user’s emotion is a step forward towards the … The backpropagation learning algorithm through a dif- ferentiable loss is a method of empirical risk mini- mization …

A Study of Information Bots and Knowledge Bots
A Hatua – 2020 – aquila.usm.edu
… their diffusion. On the other hand, chatbots are used for the study of Knowledge bots. Knowledge base plays the most critical role in developing a Goal-Oriented (GO) chatbot. A GO chatbot is as good as its knowledge base …

Transfer Hierarchical Attention Network for Generative Dialog System
X Zhang, Q Yang – International Journal of Automation and Computing, 2019 – Springer
… By training with backpropagation through time (BPTT), the fixed length vector is expec- ted to encode necessary information of the input sen- tence for … 2.1 Generative dialog system In the domain of chit-chat dialog systems, various generative models have been proposed …

Affective and Human-Like Virtual Agents
B Budnarain – 2020 – uwspace.uwaterloo.ca
… recent years. It should be noted that dialogue systems are also referred to as conversational agents or chatbots. From an alternative perspective, dialogue systems can be viewed as intelligent virtual assistive technology [14]. AI …

Incorporating Politeness across Languages in Customer Care Responses: Towards building a Multi-lingual Empathetic Dialogue Agent
M Firdaus, A Ekbal, P Bhattacharyya – Proceedings of The 12th …, 2020 – aclweb.org
… Such systems are highly prevalent nowadays in the form of chatbots and personal assistants … language generation is one of the core components of every dialogue system (Shen et al … during forward propagation, and sign inversion of the gradi- ents during backpropagation, to be …

Low-Resource Response Generation with Template Prior
Z Yang, W Wu, J Yang, C Xu, Z Li – arXiv preprint arXiv:1909.11968, 2019 – arxiv.org
… of human conversation on the internet, building an open domain dialogue system with data … automatic speech recognition (Tüske et al., 2014), task- oriented dialogue systems (Tran and … the NHSMM are estimated by maximizing the log-likelihood of DU through backpropagation …

Video Dialog via Multi-Grained Convolutional Self-Attention Context Networks
W Jin, Z Zhao, M Gu, J Yu, J Xiao… – Proceedings of the 42nd …, 2019 – dl.acm.org
… While dialog system [32–34, 39, 41] has been widely explored, visual dialog is still a young task … And recently, Hori et al. [10] propose a model that incorporates technologies for multimodal attention-based video description into an end-to-end dialog system …

ALOHA: Artificial Learning of Human Attributes for Dialogue Agents.
AW Li, V Jiang, SY Feng, J Sprague, W Zhou, J Hoey – AAAI, 2020 – cs.uwaterloo.ca
… Open-domain chatbots are more generic dialogue sys- tems … Li et al. (2016) defines persona (composite of elements of identity) as a possible solution at the word level, using backpropagation to align responses via word embeddings … 1https://github.com/newpro/aloha-chatbot …

Hierarchy Response Learning for Neural Conversation Generation
B Zhang, X Zhang – Proceedings of the 2019 Conference on Empirical …, 2019 – aclweb.org
… Last, this process cannot be efficiently optimized using stochastic gradient de- scent (SGD) akin to backpropagation on feedfor- ward neural networks … et al., 1978; Young et al., 2013; Daniel Ju- rafsky, 2017) have explored dialog act interactions with dialog system and proposed …

Developing enhanced conversational agents for social virtual worlds
D Griol, A Sanchis, JM Molina, Z Callejas – Neurocomputing, 2019 – Elsevier
… to enhance communication in these environments, we propose the integration of dialog systems to develop … to understand the user and decide what to respond, but, unlike chatbots and other … to the application domain in order to optimize the behavior of the dialog system in that …

Document-editing Assistants and Model-based Reinforcement Learning as a Path to Conversational AI
K Kudashkina, PM Pilarski, RS Sutton – arXiv preprint arXiv:2008.12095, 2020 – arxiv.org
… We separate modern dialogue systems into three cate- gories to illustrate their closeness to purposive intelligent assistants. The first category is entertainment systems that provide open-domain conversations (see Huang, Zhu, and Gao, 2020). These are chatbots and systems …

Diving Deep into Deep Learning: History, Evolution, Types and Applications
HCA Deekshith Shetty, MJ Varma, S Navi, MR Ahmed – researchgate.net
… the result will be produced. Neural networks are trained using an algorithm called backpropagation, this method does the reverse calculation of the gradient from last layer to first layer. Deep learning has many architectures …

Expanding on the end-to-end memory network for goal-oriented dialogue
PA Taraldsen, V Vatne – 2019 – uia.brage.unit.no
… of the Dialog System Technology Challenge: building an end- to-end dialog system for goal … In: Workshop: Chatbots for Social Good, September 3, 2019, Paphos, Cyprus (under review) … There are many benefits of using goal-oriented dialog systems and their applications can be …

Proposed Model for Arabic Grammar Error Correction Based on Convolutional Neural Network
A Solyman, Z Wang, Q Tao – 2019 International Conference on …, 2019 – ieeexplore.ieee.org
… The current project will be the basis for future ALNP projects such as text generation, dialog systems, and semantic … or decoder-encoder model [13], has successfully applied in applications such as online chatbots, Google Translate … [11] PJ Werbos, “Backpropagation through time …

CHAPTER FOUR DISTRIBUTIONAL AND NETWORK SEMANTICS. TEXT ANALYSIS APPROACHES ALEXANDER KHARLAMOV, DENIS GORDEEV AND DMITRY …
A KHARLAMOV – Neuroinformatics and Semantic …, 2020 – books.google.com
… So, the best chat-bot of 2007 UltraHal1 used not only search by key expressions, but also … from Microsoft6 offers only these two types of information for subsequent use by chat-bots (Fig … This is how the idea of the “backpropagation” method was born: we can calculate the gradient …

Deep learning for spoken dialogue systems: application to nutrition
MB Korpusik – 2019 – dspace.mit.edu
… partment of Defense (DoD) through the National Defense Science & Engineering Gradu- ate Fellowship (NDSEG) Program. 8 Page 9. Contents 1 Introduction 31 1.1 Dialogue Systems … 164 7.4 7th Dialogue System Technology Challenge (DSTC7) …

Deep learning for nlp and speech recognition
U Kamath, J Liu, J Whitaker – 2019 – Springer
Page 1. Uday Kamath · John Liu · James Whitaker Deep Learning for NLP and Speech Recognition Page 2. Deep Learning for NLP and Speech Recognition Page 3. Uday Kamath • John Liu • James Whitaker Deep Learning for NLP and Speech Recognition 123 Page 4 …

The Comparison Between the Tools for Named Entity Recognition
W Zhang – 2020 – openrepository.aut.ac.nz
… even “talk” with our electronic devices (chatbots). In erstwhile times … level, the range includes parsing and POS (part of speech) tagging, sequence labeling, dialogue systems, and many others … chatbot, medical analysis, detection of spam emails. Page 13. 4 …

DEEP NEURAL NETWORK MODELS FOR SEQUENCE LABELING AND COREFERENCE TASKS
BM Sergeevich – mipt.ru
… 43 2.13 Backpropagation through time in RNN … 86 3.9 Annotating step of a voice-enabled chatbot. . . . . 88 … attention from researchers and widely covered in the media. 1966 ELIZA ELIZA was one of the earliest chatbots, developed by Joseph …

Automatically responding to customers
R Huijzer – pure.tue.nl
… The IBM sales department claims that Autodesk using chatbots cut down their resolution time “from 1.5 days … Often the chatbot needs to know more than just the intent … The backpropagation algorithm updates the weights in the network by sending information from the output layer …

A Guide to the NeurIPS 2018 Competitions
R Herbrich, S Escalera – The NeurIPS’18 Competition, 2020 – Springer
… The aim of this challenge was to establish a concrete scenario for testing chatbots that aim to engage humans, and become a standard evaluation … The winning dialogue systems was chosen based on these scores … Backpropagation applied to handwritten zip code recognition …

MSc in Computer Science
RB Sulaiman – researchgate.net
… CHAPTER INFORMATION IN THIS CHAPTER ? Overview ? Chatbot system ? Chatbot technology ? Types of chatbot ? Comparison of chatbots ? Functions of chatbot ? Syntactic analysis ? Vector space model ? Machine learning models ? Support vector machines (SVM) …

Detecting Abuse on the Internet: It’s Subtle
S Bagga – 2020 – search.proquest.com
Page 1. Detecting Abuse on the Internet: It’s Subtle Sunyam Bagga Master of Science School of Computer Science McGill University Montreal, Quebec December, 2019 A thesis submitted to McGill University in partial fulfillment …

Neural Question Generation with Transfer Learning and Utilization of External Knowledge
M Delpisheh – 2020 – yorkspace.library.yorku.ca
… The generated QAs can be useful for training deep neural network models for building eg, dialog systems. QG can also benefit reading comprehension (Du, Shao, and Cardie, 2017), self … accomplish a task (eg, dialogue systems, summarization). Therefore, by considering …

Trouble on the horizon: Forecasting the derailment of online conversations as they develop
JP Chang, C Danescu-Niculescu-Mizil – arXiv preprint arXiv:1909.01362, 2019 – arxiv.org
… rather than prediction: recent work in context-aware dialog generation (or “chat- bots”) has proposed … trained on large amounts of unsuper- vised data, similarly to how chatbots are trained … com- ponent are not held fixed during this process; in- stead, backpropagation is allowed …

A Study of State Aliasing in Structured Prediction with RNNs
LE Asri, A Trischler – arXiv preprint arXiv:1906.09310, 2019 – arxiv.org
… Thus, if the policy is the same for two different states, an encoder RNN trained by policy gradients and backpropagation might represent the two … A sequence-to-sequence model for user simulation in spoken dialogue systems … understanding the low-diversity problem of chatbots …

Computer Vision in Control and Optimization of Road Traffic
V Zinchenko, G Kondratenko, I Sidenko… – 2020 IEEE Third …, 2020 – ieeexplore.ieee.org
… to educate artificial neural networks is the reverse propagation error (backpropagation) algorithm … of human machine interaction for synthesis of the intelligent dialogue chatbot,” 10th IEEE … [24] P. Kushneryk, Y. Kondratenko, and I. Sidenko, “Intelligent dialogue system based on …

Video Dialog via Multi-Grained Convolutional Self-Attention Context Multi-Modal Networks
M Gu, Z Zhao, W Jin, D Cai, F Wu – IEEE Transactions on …, 2019 – ieeexplore.ieee.org
… While dialog system [41], [42], [43], [44], [45] has been widely explored, visual dialog is still a young task … And recently, Hori et al. [54] propose a model that incorporates technologies for multimodal attention-based video description into an end-to-end dialog system …

Teaching Machines to Converse
J Li – arXiv preprint arXiv:2001.11701, 2020 – arxiv.org
… conventional dialog systems still face a variety of major challenges such as robustness … Specifically, we will discuss three types of dialogue systems: the chit-chat system, the frame-based goal oriented system, and the interactive question-answering (QA) dialogue system …

Artificial Intelligence in Daily Life
RST Lee – 2020 – Springer
… After that, we will study two innovative AI technologies applied to healthcare: (1)health chatbot and (2)robot-assisted surgery (RAS) technology. • Chapter 11—Smart Education This chapter begins with smart education progress in the past decades …

Towards Diversity and Relevance in Neural Natural Language Response Generation
D Handloser – 2020 – isl.anthropomatik.kit.edu
… DPG Deterministic Policy Gradient DSTC7 7th Dialog System Technology Challenges … backpropagation [30] … Open-domain dialogue system, as is covered in this work, do not only solve one speci c task, therefore they are sometimes referred to as non-goal-driven …

Early integration for movement modeling in latent spaces
R Hornung, N Chen, P van der Smagt – The Handbook of Multimodal …, 2019 – dl.acm.org
Page 1. 8Early Integration for Movement Modeling in Latent Spaces Rachel Hornung, Nutan Chen, Patrick van der Smagt 8.1 Introduction In this chapter, we will show how techniques of advanced machine and deep learn- ing …

Efficient Algorithm for Answering Fact-based Queries Using Relational Data Enriched by Context-Based Embeddings
AA Altowayan – 2019 – webpage.pace.edu
… Page 3. Abstract Intelligent conversational systems – such as question answering and chatbots – are becoming a more critical component of today’s AI in areas ranging from health, medicine, and security, to personal assistants, and other domains …

Neural Language Generation: Formulation, Methods, and Evaluation
C Garbacea, Q Mei – arXiv preprint arXiv:2007.15780, 2020 – arxiv.org
… Examples of attributes used for conditioning the generated text are the source sentence in machine translation, the con- versational history in dialogue systems, the input document in text summarization and text simpli- fication, the input question in question answering systems …

Learning to Converse With Latent Actions
T Zhao – 2019 – lti.cs.cmu.edu
… 2.1 Dialog system pipeline for task-oriented dialog systems . . . . . 8 … These unique features make latent action E2E dialog system powerful and practical for creating dialog systems in a variety of usage and domains. 1.2 Thesis Statement …

Transformer-Based Observers in Psychotherapy
T Sunkaraneni – 2020 – cs.utah.edu
… 8 2.4 Backpropagation through time … Conversational agents have been implemented in the field of psychotherapy for a relatively long time, going back to chatbots such as ELIZA … dialogue systems. Pre-trained transformer models are usually trained on dumps of the Page 13. 3 …

A comparative study of word embedding methods for early risk prediction on the Internet
E Fano – 2019 – diva-portal.org
… Another realistic scenario would be to provide support for help lines and hospitals. It would be possible to develop chat bots and other dialogue systems that can determine the severity of a person’s mental health risk based on just a few lines of text …

Improving Top-Down Generation of Natural Language with Hierarchy
DJ Donahue – 2020 – search.proquest.com
… et al., 2019) also attempts to reduce the computation for longer input sequences by segmenting the input into chunks and avoiding backpropagation through previous chunks … they can run increasingly longer sequence lengths without increasing the run time of backpropagation …

AI and IoT in Healthcare
S Singla – Internet of Things Use Cases for the Healthcare …, 2020 – Springer
… neuroscience and material science (with learning calculations dependent on backpropagation), hereditary qualities … Woebot is an AI-sponsored chatbot that enables individuals to examine their … that can play out these underlying analytic evaluations—regularly as chatbots over a …

Condition-Transforming Variational Autoencoder for Generating Diverse Short Text Conversations
YP Ruan, ZH Ling, X Zhu – ACM Transactions on Asian and Low …, 2020 – dl.acm.org
… Natural language conversation, which can be categorized into task-oriented dialog [45] and non- task-oriented chatbot, is among … To guarantee the feasibility of error backpropagation for model training, reparametrization [14] is performed to generate the samples of t. To derive …

InPHYNet: Leveraging attention-based multitask recurrent networks for multi-label physics text classification
V Udandarao, A Agarwal, A Gupta, T Chakraborty – Knowledge-Based Systems – Elsevier
… to-use form that can be utilized to build a question-answering physics chatbot, we prepared … an increasing focus on building end-to-end QA based interactive dialog systems for facilitating … of work has been pivoted on the Student Response Analysis (SRA) part of a dialog system …

Socially-Aware Dialogue System
R Zhao – 2019 – lti.cs.cmu.edu
… However, social chatbots fall short in replicating the interpersonal function of communication … SAPA) This chapter reviews our knowledge-inspired socially-aware dialogue system in a … recognition of conversational strategies in the service of a socially-aware dialog system …

AInix: An open platform for natural language interfaces to shell commands
D Gros – 2019 – cs.utexas.edu
… and be able to support varying interfaces such as voice assistants, chatbots, or video … Such an approach is popular in commercially deployed dialog systems like Amazon Alexa (Kumar et al … This model is trained end-to-end with backpropagation to maximize the likelihood the …

Crsal: Conversational recommender systems with adversarial learning
X Ren, H Yin, T Chen, H Wang, NQV Hung… – ACM Transactions on …, 2020 – dl.acm.org
… 2.2 Task-oriented Dialogue Systems Task-oriented dialogue systems are one important branch in dialogue system research, which aims to help the user finish some specific tasks [96]. Though conversational recommendation …

Proactive Communication in Human-Agent Teaming
EM van Zoelen – 2019 – dspace.library.uu.nl
… In chatbot building tools, these states are usually called ‘intents’ … Because of this, the resulting dialogue system can only be used for very narrow domains and is not flexible in … in many narrow-domain applications, which is why they are still widely used, for example for chatbots …

Improving conditional sequence generative adversarial networks by stepwise evaluation
YL Tuan, HY Lee – IEEE/ACM Transactions on Audio, Speech …, 2019 – ieeexplore.ieee.org
… To meet the necessary of large dataset for chatbot, reward at every genera- tion step (REGS) is proposed to replace MCTS but with … 8], REGS [2], WGAN-GP [18], [19], TextGAN [20], RankGAN [21] and MaskGAN [9]. To conquer the intractable backpropagation through discrete …

World Knowledge Representation
Z Liu, Y Lin, M Sun – Representation Learning for Natural Language …, 2020 – Springer
… graph representation, which represents entities and relations in knowledge graphs with distributed representations, has been proposed and applied to various real-world artificial intelligence fields including question answering, information retrieval, and dialogue system …

Variational Inference for Text Generation: Improving the Posterior
V Balasubramanian – 2020 – uwspace.uwaterloo.ca
… Dialogue Response Generation: This task is quite common in chatbots and other similar services … The required gradients can be computed by a technique known as backpropagation (Rumelhart et al., 1986) which makes use of the chain rule of derivatives …

Dialog Response Generation Using Adversarially Learned Latent Bag-of-Words
K Khan – 2020 – uwspace.uwaterloo.ca
… An optimiza- tion algorithm such as stochastic gradient descent (SGD) along with an automatic differentiation technique called backpropagation is used to train the network. The function being approximated by the neural network is called the loss function …

Syntactically Guided Text Generation
Y Li – 2020 – smartech.gatech.edu
… linguistic representation of information [3] and includes applications such as weather fore- casts generation [4], article or database summarization [5, 6, 7], question answering (QA) [8], dialog generation (chatbot) [9], machine translation (MT) [10], etc. Pioneered by the …

Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Healthcare Applications: 10th International Conference, DHM …
VG Duffy – 2019 – books.google.com
… Fernando Fabbrini, André Souza, Guilherme Fidélio, Juliana Machado, and Rachel Sarra Mobile Phone-Based Chatbot for Family … 391 Shota Nakatani, Sachio Saiki, Masahide Nakamura, and Kiyoshi Yasuda Design of Coimagination Support Dialogue System with Pluggable …

Biomedical Text Dependency Parsing with the Neural Turku Parser
T Ngo Minh – 2020 – doria.fi
… tion, it is an essential building block for many other NLP tasks, for example machine translation (Galley and Manning, 2009) and dialogue system (Sugiyama et al., 2013). In … in Hochreiter’s diploma thesis (Hochreiter, 1991). Simply put, when backpropagation …

Offline reinforcement learning: Tutorial, review, and perspectives on open problems
S Levine, A Kumar, G Tucker, J Fu – arXiv preprint arXiv:2005.01643, 2020 – arxiv.org
… Other model-based reinforcement learning methods utilize a learned policy ??(at|st) in addition to the dynamics model, and employ backpropagation through time to optimize the policy with respect to the expected reward objective (Deisenroth and Rasmussen, 2011) …

Suicidal ideation detection in online social content
S Ji – 2020 – researchgate.net
… which was later discontinued because of privacy issues. The latter is a Facebook chatbot based on … I2 Il In hn h2 h1 y d(t, y) E MSE Update of weights Backpropagation (a) Neural network with feature engineering + Title Text Body Statis POS LIWC TF-IDF Topics LIWC POS Statis …

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