Natural Language Generation, Deep Neural Networks & Chatbots

Notes:

Compositional semantics is a branch of linguistics that deals with the meaning of sentences and larger linguistic units, and how these meanings are derived from the meanings of individual words and smaller units. It is concerned with understanding how the meanings of words combine to form the meanings of sentences and longer utterances, and how these meanings are conveyed in natural language.

Natural language generation (NLG) is a subfield of artificial intelligence (AI) that involves the use of computers to automatically produce written or spoken language that is intended to be similar to human language. NLG systems often rely on compositional semantics to understand how to generate appropriate sentences and utterances based on the meanings of individual words and smaller units.

Deep neural networks (DNNs) are a type of machine learning model that are inspired by the structure and function of the human brain. They are composed of multiple interconnected layers of artificial neurons, and are able to learn and make decisions based on patterns and relationships in data. DNNs are sometimes used in NLG systems to help generate human-like language, and they may be able to learn and apply compositional semantics to understand how to generate appropriate sentences and utterances.

Chatbots are computer programs that are designed to simulate conversation with human users, either via text-based messaging or through voice interactions. They are often used in customer service and support roles, and may rely on NLG and compositional semantics to understand and respond appropriately to user input.

Language and perception are closely related, as language plays a significant role in how we perceive and understand the world around us. Our use of language shapes and is shaped by our perception and understanding of reality. For example, the words and phrases we use to describe objects and events can influence how we perceive those objects and events. Similarly, our perceptions and understanding of the world can shape the language we use to describe it. Additionally, language can be used to convey abstract concepts and ideas that may not have a direct physical manifestation, and our understanding of these concepts and ideas is influenced by the language used to describe them.

Perception is the process of interpreting and organizing sensory information in order to understand and navigate the environment. It involves the brain receiving information from the senses (such as sight, hearing, touch, taste, and smell), and then processing that information to create a meaningful experience. Consciousness is the subjective experience of being aware of one’s thoughts, feelings, and surroundings.

There is a close relationship between perception and consciousness, as perception plays a significant role in shaping one’s conscious experience. Our perception of the world around us helps to create our reality, and our conscious experience is largely influenced by the information that we receive through our senses.

For example, when we see an object, our brain processes visual information about its shape, color, and movement, and this information is integrated with our past experiences and knowledge to create a perception of what the object is. This perception is then consciously experienced as part of our reality.

Similarly, when we hear a sound, our brain processes auditory information about its pitch, volume, and timbre, and this information is integrated with our past experiences and knowledge to create a perception of what the sound is. This perception is then consciously experienced as part of our reality.

Language and consciousness are closely related, as language is a key tool that we use to process and make sense of our experiences and perceptions. Language allows us to describe and communicate our perceptions to others, and it also shapes the way we perceive and understand the world around us. For example, the words and concepts we use to describe objects and events can influence our perception of them and the way we think about them. Similarly, the way we talk about our experiences can shape our memories and understanding of them. In this way, language can play a central role in shaping and influencing our consciousness.

While language and perception are both important factors in our conscious experience, they are not the same as consciousness itself. NLP can be used to analyze and understand language and perception, but it does not have the ability to directly access or manipulate consciousness.

Resources:

Wikipedia:

References:

See also:

100 Best Deep Learning Cloud Videos | 100 Best Deep Learning Tutorial Videos | 100 Best GitHub: Deep Learning | 100 Best Natural Language Deep Learning Videos | Deep Belief Network & Dialog Systems | Deep Learning & Chatbots | Deep Inference 2019 | Deep Reasoning & Dialog Systems | DNLP (Deep Natural Language Processing)

Peculiarities of Human Machine Interaction for Synthesis of the Intelligent Dialogue Chatbot
I Sidenko, G Kondratenko, P Kushneryk… – 2019 10th IEEE …, 2019 – ieeexplore.ieee.org
… rule, uses deep learning techniques in each component of the dialogue system and demonstrates … One of the limitations of deep neural networ3s is that their input and output … Natural Language Generation (NLG) receives a specification of a communicative act from the dialogue …

Unified language model pre-training for natural language understanding and generation
L Dong, N Yang, W Wang, F Wei, X Liu… – Advances in Neural …, 2019 – papers.nips.cc
… new UNIfied pre-trained Language Model (UNILM) that can be applied to both natural language understanding (NLU) and natural language generation (NLG) tasks … task fine-tuning on both NLU and NLG tasks, which is a natural extension of Multi-Task Deep Neural Network (MT …

Survey on Intelligent Chatbots: State-of-the-Art and Future Research Directions
EH Almansor, FK Hussain – … on Complex, Intelligent, and Software Intensive …, 2019 – Springer
… Henderson, M., Thomson, B., Young, S.: Deep neural network approach for the dialog … Galley, M., Fosler-Lussier, E., Potamianos, A.: Hybrid natural language generation for spoken dialogue … of user satisfaction using N-gram-based dialog history model for spoken dialog system …

Intelligent Dialogue System Based on Deep Learning Technology
I Sidenko – pdfs.semanticscholar.org
… 3. Natural Language Generation (NLG) receives a specification of a communicative act from the dialog manager and generates a corresponding text representation … In this work authors showed full process of development dialog system using Deep Neural Network from …

PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules
Y Jang, J Lee, J Park, KH Lee, P Lison… – Proceedings of the 2019 …, 2019 – aclweb.org
… 4.2 RNN-based Natural Language Generation Model … Mas- tering the game of Go with deep neural networks and tree search. Nature, pages 484–489, 2016 … Demon- stration of AT&T “let’s go”: A production-grade sta- tistical spoken dialog system …

User attention-guided multimodal dialog systems
C Cui, W Wang, X Song, M Huang, XS Xu… – Proceedings of the 42nd …, 2019 – dl.acm.org
… improving task- oriented dialog systems in this work, especially the multimodal dialog system in the … NLU); 2) dialog state tracker (DST); 3) policy network; and 4) natural language generation (NLG) … a picture is worth a thousand words”, yet most existing dialog systems only focus …

Multi-representation fusion network for multi-turn response selection in retrieval-based chatbots
C Tao, W Wu, C Xu, W Hu, D Zhao, R Yan – Proceedings of the Twelfth …, 2019 – dl.acm.org
… KEYWORDS Fusing multiple representations; deep neural network; matching; multi-turn response selection; retrieval … data on social media, building a non-task-oriented chatbot with data … the latter directly synthesize a re- sponse via natural language generation techniques [24, 25 …

Comparing cross language relevance vs deep neural network approaches to corpus-based end-to-end dialogue systems
SH Alavi, A Leuski, D Traum – … of the 23rd Workshop on the …, 2019 – www-scf.usc.edu
… internal representation language), updating dialogue state, state-based response generation, and natural language generation (eg, (Traum … From the pool of previous deep neural net mod- els, such as (Hochreiter and Schmidhuber … taking a spoken dialog system to the real world …

History and Development Tendency of Human-Computer Dialogue System
X Lin, T Yuan, G Lei – … on Artificial Intelligence and Big Data …, 2019 – ieeexplore.ieee.org
… NLU), Dialogue State Tracker (DST), Dialogue Policy Learning, and Natural Language Generation (NLG) … Reference [41] also proposed a deep neural network to solve the context-aware … B. Thomson, and JD Williams, “POMDP- Based statistical spoken dialog systems: a review …

Multimodal dialog system: Generating responses via adaptive decoders
L Nie, W Wang, R Hong, M Wang, Q Tian – Proceedings of the 27th ACM …, 2019 – dl.acm.org
… To address the aforementioned challenges, in this work, we present a Multimodal diAloG system with adaptIve … [43] proposed a deep neural matching network … Ultimately, the natural language generation component gives the response through the predefined templates or some …

Open Domain Chatbot Based on Attentive End-to-End Seq2Seq Mechanism
SS Abdullahi, S Yiming, A Abdullahi… – Proceedings of the 2019 …, 2019 – dl.acm.org
… networks, execute one or more related commands and provide feedback via Natural language generation (NLG) [1 … This study show that a simple language model based on deep neural seq2seq with … Learning to Select Knowledge for Response Generation in Dialog Systems …

A Pipeline-Based Task-Oriented Dialogue System on DSTC2 Dataset
Y Pang – 2019 – utd-ir.tdl.org
… The natural language generation (NLG) component learns the mapping between dialogue … Page 19. a dialogue system as a whole and usually model it through one end-to-end neural network … similarity. Deep Neural Networks (DNN) are widely used in this area …

Survey of Textbased Chatbot in Perspective of Recent Technologies
B Som, S Nandi – … Conference, CICBA 2018, Kalyani, India, July …, 2019 – books.google.com
… A dialog system typically requires four components: a preprocessing component, a natural … In the field of statistical machine translation (SMT), deep neural networks have … Technologies 91 4.4 Natural Language Generation The natural language generation component converts …

A Survey on Reinforcement Learning for Dialogue Systems
I Graßl – pdfs.semanticscholar.org
… Index Terms—reinforcement learning, dialogue system, chat- bot, conversational agent, human-computer-interaction … Output Modules (Natural Language Generation) … I., Panneershelvam, V., Lanctot, M. (2016): Mastering the game of go with deep neural networks and tree search …

Natural language understanding for dialogue systems using n-best lists
S Mansalis – MS thesis, 2019 – aueb.gr
… system researchers both in research and industry. Dialogue systems are generally divided into … and speed during implementing and building deep neural network architectures. A model … Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages …

A Compression-based BiLSTM for Treating Teenagers’ Depression Chatbot
J YIN – DEStech Transactions on Computer Science and …, 2019 – dpi-proceedings.com
… [10] Collobert R, Weston J. A unified architecture for natural language processing: Deep neural networks with multitask … [14] Wen TH, Gasic M, Mrksic N, et al. Semantically conditioned lstm-based natural language generation for spoken dialogue systems[J]. arXiv preprint …

Deep reinforcement learning for chatbots using clustered actions and human-likeness rewards
H Cuayáhuitl, D Lee, S Ryu, S Choi… – … Joint Conference on …, 2019 – ieeexplore.ieee.org
… During each verbal contribution, the DRL agent (1) observes the state of the world via a deep neural network, which models a representation of all … This is not surprising given the fact that task-oriented dialogue systems use finite action sets, while chatbot systems use …

Ordinal and Attribute Aware Response Generation in a Multimodal Dialogue System
H Chauhan, M Firdaus, A Ekbal… – Proceedings of the 57th …, 2019 – aclweb.org
… Ordinal and Attribute Aware Response Generation in a Multimodal Dialogue System … discuss some of the prominent research car- ried out on single and multi-modal dialog systems … Deep neural models have been quite ben- eficial for modelling conversations in (Vinyals and Le …

Multi-Turn Response Selection in Retrieval-Based Chatbots with Iterated Attentive Convolution Matching Network
H Wang, Z Wu, J Chen – Proceedings of the 28th ACM International …, 2019 – dl.acm.org
… matching; retrieval-based chatbot; multi-turn response selection; deep neural network ACM … based approach that directly generates a response via natural language generation techniques [19 … conversation data available, a number of data-driven dialogue systems are designed …

Recurrent neural models and related problems in natural language processing
S Zhang – 2019 – papyrus.bib.umontreal.ca
… how to perform advanced multi-hop reasoning in machine reading comprehension and how to encode person- alities into chitchat dialogue systems … The fourth article tackles the problem of the lack of personality in chatbots … cessing, reading comprehension, dialogue system v …

Short Text Conversation Based on Deep Neural Network and Analysis on Evaluation Measures
HE Cherng, CH Chang – arXiv preprint arXiv:1907.03070, 2019 – arxiv.org
… Keywords—short text conversation, nugget detection, dialogue quality, deep neural networks, memory … Retrieval-based dialogue system requires a knowledge base with large amount of … Researchers focus on machine learning methods to build dialogue systems, all rules and …

Learning from dialogue after deployment: Feed yourself, chatbot!
B Hancock, A Bordes, PE Mazare, J Weston – arXiv preprint arXiv …, 2019 – arxiv.org
… conversations the chat- bot participates in are sliced into two complemen- tary datasets—one largely protected from the chat- bot’s mistakes (DIALOGUE … empirically that regardless of the num- ber of available supervised examples, the dia- logue ability of the chatbot is always …

Lifelong learning and task-oriented dialogue system: what does it mean?
M Veron, S Ghannay, AL Ligozat, S Rosset – 2019 – hal.archives-ouvertes.fr
… of three modules: natural language understanding (NLU), dialogue management and natural language generation (NLG) … The NLU module is based on deep neural network perform- ing both slot-filling and … Lifelong learning and task-oriented dialogue system: what does it mean …

AI-Powered Text Generation for Harmonious Human-Machine Interaction: Current State and Future Directions
Q Zhang, B Guo, H Wang, Y Liang… – 2019 IEEE SmartWorld …, 2019 – ieeexplore.ieee.org
… Keywords—text generation, deep learning, dialog system I. INTRODUCTION Text generation is an … Different from deep neural networks (DNN) and convolutional neural networks (CNN), RNN … It can be divided into two steps, feature extraction and natural language generation …

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 … Natural Language Generation (NLG), which turns a system action into natural language and outputs it to the … With the success of deep neural networks (DNN) in computer vision, a couple of …

An Approach to Teach Nao Dialogue Skills
M Graña, A Triguero – International Work-Conference on the Interplay …, 2019 – Springer
… Generator, formulates the response in correct language constructs by Natural Language Generation techniques, and … We are working towards the implementation of a dialog system that learns from … H.: Convolutional, long short-term memory, fully connected deep neural networks …

Goal-Oriented Conversational System Using Transfer Learning and Attention Mechanism
A Hatua, TT Nguyen, AH Sung – 2019 IEEE 10th Annual …, 2019 – ieeexplore.ieee.org
… Language Understanding (NLU), (2) State tracker, (3) Dialogue policy, (4) Natural Language Generation (NLG … To use a fully data-driven deep neural network, all these four modules must be … B. Thomson and JD Williams, “Pomdp-based statistical spoken dialog systems: A review …

A sequential matching framework for multi-turn response selection in retrieval-based chatbots
Y Wu, W Wu, C Xing, C Xu, Z Li, M Zhou – Computational Linguistics, 2019 – MIT Press
… Dialog systems focus on helping people complete specific tasks in vertical domains (Young et al … Generation-based chatbots reply to a message with natural language generation techniques … in its previous turns and performed matching with a deep neural network architecture …

Retrieval-based Goal-Oriented Dialogue Generation
AV Gonzalez, I Augenstein, A Søgaard – arXiv preprint arXiv:1909.13717, 2019 – arxiv.org
… 2013. Deep neural network approach for the dialog state tracking challenge … End-to-end task-completion neural dialogue systems. In IJCNLP … How not to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation …

A hybrid retrieval-generation neural conversation model
L Yang, J Hu, M Qiu, C Qu, J Gao, WB Croft… – Proceedings of the 28th …, 2019 – dl.acm.org
… growing field referred to as Conversational AI [7]. Typical task-oriented dialog systems use a … a dialog state tracker, a dialog policy learning module, and a natural language generation module [11] … The interaction matrix is further fed into deep neural networks which could be a …

BoFGAN: Towards A New Structure of Backward-or-Forward Generative Adversarial Nets
MKS Chen, X Lin, C Wei, R Yan – The World Wide Web Conference, 2019 – dl.acm.org
… 1 INTRODUCTION Natural Language Generation (NLG) is the process of producing high-quality texts from … step in many applications, such as machine translation [1, 33], dialogue systems [36, 38 … In particular, deep neural networks (DNNs) lead the way since they can learn …

Self-Attentional Models Application in Task-Oriented Dialogue Generation Systems
M Saffar Mehrjardi – 2019 – era.library.ualberta.ca
… NER Named-entity Recognition NLG Natural Language Generation NLI Natural Language Inference x Page 11 … engaged when they feel that they have become friends with the chatbot. Ama … chatbots, and customer-service chatbots. Deployment of task-oriented chat …

Emotion-Eliciting Poetry Generation
B Bena, J Kalita – cs.uccs.edu
… Vaswani et al. (2017) developed a deep neural architecture called the Transformer that did away with any sort of need for recurrence … A survey of natural language generation techniques with a focus on dialogue systems-past, present and future directions …

End-to-End Personalized Humorous Response Generation in Untrimmed Multi-Role Dialogue System
Q Yang, Z He, Z Zhan, R Li, Y Lee, Y Zhang… – IEEE Access, 2019 – ieeexplore.ieee.org
… for interaction between humans and machines, making them vital components of chatbots and personal … to-speaker relationships is a challenge for constructing a more intelligent dialogue system. A few role-related dialogue systems have been proposed recently, such as multi …

Response generation by context-aware prototype editing
Y Wu, F Wei, S Huang, Y Wang, Z Li, M Zhou – Proceedings of the AAAI …, 2019 – aaai.org
… Related Work Research on chatbots goes back to the 1960s when ELIZA was designed (Weizenbaum 1966) with a huge amount of … Recently, some researches have explored natural language generation by editing (Guu et al. 2017; Liao et al. 2018) …

c-TextGen: Conditional Text Generation for Harmonious Human-Machine Interaction
B Guo, H Wang, Y Ding, S Hao, Y Sun, Z Yu – arXiv preprint arXiv …, 2019 – arxiv.org
… 3 CONDITIONAL TEXT GENERATION e development of deep neural networks brings unprecedented progress to text generation … Goal-oriented dialog systems … Luo et al. [43] build a personalized goal-oriented dialog system to complete the restaurant reservation task …

Neural conversation generation with auxiliary emotional supervised models
G Zhou, Y Fang, Y Peng, J Lu – ACM Transactions on Asian and Low …, 2019 – dl.acm.org
… 1.2.7 [Artificial Intelligence]: Natural Language Processing—Natural language generation; Emotional conversation … Because the expanding technology of deep neural networks facilitates various applications … ground truth as a comparator for emotional dialogue system evaluation …

Enhancing generative conversational service agents with dialog history and external knowledge
Z Wang, Z Wang, Y Long, J Wang, Z Xu… – Computer Speech & …, 2019 – Elsevier
… service oriented dialog corpus means a great deal for constructing chatbots with bland … different methods of exploiting the dialog history related information in a dialog system … the methodology to improving the informativeness of responses given by generative dialog systems …

FinBrain: when finance meets AI 2.0
X Zheng, M Zhu, Q Li, C Chen, Y Tan – Frontiers of Information Technology …, 2019 – Springer
… Recent state-of-the-art recommendation models adopt deep neural networks (DNNs) to effectively model … systems consist of a natural language understanding module, a natural language generation module, and … (2017) presented a task- completion dialogue system to complete …

Evaluation Methods of Emotional Expression in Short Text Conversation
SH Wu, WF Shih, SL Chien – 2019 – research.nii.ac.jp
… It is difficult to use automated assessments in the evaluation dialog system … With excellent results in machine translation and natural language generation, Seq2Seq [15][16] is a … of Seq2Seq’s architectures that solves the problems encountered by deep neural networks (DNNs …

Conversational AI: An Overview of Methodologies, Applications & Future Scope
P Kulkarni, A Mahabaleshwarkar… – 2019 5th …, 2019 – ieeexplore.ieee.org
… al used DNN (Deep Neural Network) and Restricted Boltzmann Machine (RBM) to … Natural Language Understanding, Dialogue Management and Natural Language Generation … Nakamura, “Statistical dialog management applied to WFST- based dialog systems,” 2009 IEEE …

Deep Reinforcement Learning for Task-Oriented Dialogue
N Iregbulem, S Yakhmi – pdfs.semanticscholar.org
… to combine reinforcement learning with traditional architectures for neural network-based natural language generation to varying … action policy) of a hand-tuned, chatbot FSM with a deep neural architecture … A network-based end-to-end trainable task-oriented dialogue system …

An attentive survey of attention models
S Chaudhari, G Polatkan, R Ramanath… – arXiv preprint arXiv …, 2019 – arxiv.org
… Question Answering, Sentiment Analysis, Part-of-Speech tagging, Constituency Parsing and Dialogue Systems … provide a tool for visualizing attention weights of deep-neural networks … attention modeling in three appli- cation domains: (i) Natural Language Generation(NLG), (ii …

Proceedings of the 2019 Workshop on Widening NLP
A Axelrod, D Yang, R Cunha, S Shaikh… – Proceedings of the 2019 …, 2019 – aclweb.org
… Speech Recognition for Tigrinya language Using Deep Neural Network Approach Hafte Abera … KB-NLG: From Knowledge Base to Natural Language Generation Wen Cui, Minghui Zhou … Evaluating Coherence in Dialogue Systems using Entailment Nouha Dziri, Ehsan Kamalloo …

Reinforcement learning for Dialogue Systems optimization with user adaptation.
N Carrara – 2019 – tel.archives-ouvertes.fr
… The first proposed approach involves clustering of Dialogue Systems (tailored for their respective user) based on their behaviours … The second idea states that before using a dedicated Dialogue System, the first in- teractions with a user should be handled carefully by a safe …

MOLI: Smart Conversation Agent for Mobile Customer Service
G Zhao, J Zhao, Y Li, C Alt, R Schwarzenberg… – Information, 2019 – mdpi.com
… Although question answering and dialog systems recently received a lot of attention, work … Figure 2 gives an overview of the overall architecture of the dialog system … DM action choices were passed through a template text-based natural language generation component (NLG …

Intent Based Utterance Segmentation for Multi IntentNLU
A Sethupat Radhakrishna – 2019 – conservancy.umn.edu
… some operation on backend databases • Natural Language Generation: If the policy chooses to respond to the user, this … ented Dialog Systems. We further focus on the intent recognition aspect of NLU. Li [2] … functioning of the Dialog system …

Intent Based Utterance Segmentation for Multi IntentNLU
AS Radhakrishna – 2019 – search.proquest.com
… some operation on backend databases • Natural Language Generation: If the policy chooses to respond to the user, this … ented Dialog Systems. We further focus on the intent recognition aspect of NLU. Li [2] … functioning of the Dialog system …

Review Response Generation in E-Commerce Platforms with External Product Information
L Zhao, K Song, C Sun, Q Zhang, X Huang… – The World Wide Web …, 2019 – dl.acm.org
… Recently, deep neural network based methods have widely used in many natural language generation (NLG) tasks and have achieved great success, such as machine translation [3, 4, 10, 26], dialogue generation [23, 24], and text summarization [17, 22, 30] …

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 … the subcomponents of the language generation process, in the next subsection we cover deep neural networks and the recent surge in these architectures towards solving natural language generation problem …

Survey on evaluation methods for dialogue
JM Deriu, A Rodrigo, A Otegi, E Guillermo, S Rosset… – 2019 – digitalcollection.zhaw.ch
… Finally the output of the dialogue manager is passed to a natural language generation (NLG) component … briefly introduce the modules of the pipelined architecture and the deep neural network based … the next component (see Figure 2). The input of the dialogue system is either …

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language …
K Inui, J Jiang, V Ng, X Wan – Proceedings of the 2019 Conference on …, 2019 – aclweb.org
Page 1. EMNLP-IJCNLP 2019 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing Proceedings of the Conference November 3–7, 2019 Hong Kong, China Page 2 …

Sketch-Fill-AR: A Persona-Grounded Chit-Chat Generation Framework
M Shum, S Zheng, W Kry?ci?ski, C Xiong… – arXiv preprint arXiv …, 2019 – arxiv.org
… Even now, the natural language generation component often uses hand-crafted tem- plates and rules … Early conversational dialogue systems such as ELIZA (Weizenbaum et al., 1966) and Alice (Wallace … We mainly focus on deep neural networks, a model class that has recently …

Developing context-aware dialoguing services for a cloud-based robotic system
JY Huang, WP Lee, TA Lin – IEEE Access, 2019 – ieeexplore.ieee.org
… Our work has several unique features: it trains a deep neural model to generate … Although non-goal-oriented dialogue systems (ie, chatbots) have recently become popular, they aimed … Essentially, the dialogue system includes a knowledge base with domain questions and the …

Situated interaction
D Bohus, E Horvitz – The Handbook of Multimodal-Multisensor Interfaces …, 2019 – dl.acm.org
… at dialog between computers and people were text-based dialog systems, such as Eliza [Weizenbaum 1966], a pattern- matching chat-bot that emulated a … Most work in spoken dialog systems has traditionally focused on dyadic settings, where a dialog system interacts with a …

Deep Reinforcement Learning for Text and Speech
U Kamath, J Liu, J Whitaker – Deep Learning for NLP and Speech …, 2019 – Springer
… Since deep neural networks tend to be unstable estimators of the state value function, deep … survey of different DRL methods for information extraction, text classification, dialogue systems, text summarization, machine translation, and natural language generation …

Computational Intelligence in Conversational UI, A BotLibre Case Study. A survey paper
BA Kumar – engrxiv.org
… conversational UI are typically used in dialog systems for various practical purposes … Natural language generation Convert information from computer databases or semantic intents into readable … Deep learning architectures such as deep neural networks, deep belief networks …

Data Science and Conversational Interfaces: A New Revolution in Digital Business
D Griol, Z Callejas – Data Science and Digital Business, 2019 – Springer
… Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups … 19. Ota, R., & Kimura, M. (2014). Proposal of open-ended dialog system based on topic maps … Building applied natural language generation systems …

Proposal of a Hybrid Approach for Natural Language Generation and its Application to Human Language Technologies
C Barros – 2019 – rua.ua.es
Page 1. Proposal of a Hybrid Approach for Natural Language Generation and its Application to Human Language Technologies … Escuela Politécnica Superior Proposal of a Hybrid Approach for Natural Language Generation and its Application to Human Language Technologies …

Dynamic Working Memory for Context-Aware Response Generation
Z Xu, C Sun, Y Long, B Liu, B Wang… – … on Audio, Speech …, 2019 – ieeexplore.ieee.org
… Therefore, the proposed deep neural architecture is a memory framework for modeling context … End-to-End Conversation Modeling track of the Dialog System Technology Challenges … current widely utilized auto- matic metrics for evaluating natural language generation in- clude …

Adversarial training in affective computing and sentiment analysis: Recent advances and perspectives
J Han, Z Zhang, B Schuller – IEEE Computational Intelligence …, 2019 – ieeexplore.ieee.org
… Neural Networks (CNNs) have been reported to considerably out- perform conventional models and non- deep neural networks on … of Generated Emotions Emotion synthesis and conversion go beyond the conventional constructs of Natural Language Generation (NLG), Text-To …

Deep learning for nlp and speech recognition
U Kamath, J Liu, J Whitaker – 2019 – Springer
… The chapter begins with a fundamental anal- ysis of the components of deep learning in the multilayer perceptron (MLP), followed by variations on the basic MLP architecture and techniques for training deep neural networks …

Many vs. Many Query Matching with Hierarchical BERT and Transformer
Y Xu, Q Liu, D Zhang, S Li, G Zhou – CCF International Conference on …, 2019 – Springer
… due to its widely-used applications, such as response selection in dialogue system [1] and … He, H., Lin, J.: Pairwise word interaction modeling with deep neural networks for semantic similarity … Zhang, H., et al.: Pretraining-based natural language generation for text summarization …

IMPLEMENTATION OF AN AUTOMATIC QUESTION ANSWERING SYSTEM USING MACHINE LEARNING
SA ABIR – 2019 – researchgate.net
… The seq2seq model is originally proposed for machine translation and later adapted to various natural language generation tasks, such … mainly from the knowledge sources, the broadness of Dialog Systems (NLDS) is an … Learning, Neural Network and Deep Neural Networks …

Dynamic Search–Optimizing the Game of Information Seeking
Z Tang, GH Yang – arXiv preprint arXiv:1909.12425, 2019 – arxiv.org
… Finally, a natural language generation (NLG) com- ponent generates the chatbot’s responsive utterances from … to-end with supervised machine learning methods, especially with deep neural networks … Based on the way in which the dialogue system generates/selects its response …

A Readiness Evaluation of Applying e-Government in the Society: Shall Citizens begin to Use it?
LT Khrais, MA Mahmoud… – Editorial Preface From …, 2019 – researchgate.net
… This project centers around the study of deep learning models, natural language generation, and the … A. Chatbots Applications and Uses Artificial dialogue systems are interactive talking machines called … Chatbot applications have been around for a long time; the first well-known …

Data fusion methods in multimodal human computer dialog
Y Ming-Hao, TAO Jian-Hua – Virtual Reality & Intelligent Hardware, 2019 – Elsevier
… Non-Task-Oriented multi-modal human computer dialog system is also called open … Most researchers adopt deep neural network to generate the sentences from large … out natural language understanding, reasoning, decision making and natural language generation in order to …

VQAG: Automatic Generation of Question-Answer Pairs from Images
Z Wang – 2019 – cs.anu.edu.au
… propose a multi-modal system learning from natural methods that human receive information and take advantages of deep neural networks that … as intelligent education, chatbot and smart home system, taking advantages of its ability to automatically ask and answer questions …

A context-aware conversational agent in the rehabilitation domain
T Mavropoulos, G Meditskos, S Symeonidis… – Future Internet, 2019 – mdpi.com
… most contemporary studies (References [14,15]) presenting promising results by utilising Deep Neural Network (DNN … Hidden Markov Model (HMM)-based models [17] and incremental dialogue systems [18 … An essential unit of any dialogue system is the module that manages the …

Humour-in-the-loop: Improvised Theatre with Interactive Machine Learning Systems
KW Mathewson – 2019 – era.library.ualberta.ca
… abstract and poster: Mathewson KW and Mirowski P. (2017) Artificial Im- provisation: Improvisational Theatre with Deep Neural Networks and Robots … and understanding the conversational dialogue system research, and Section … systems, and improvised theatre …

Contextual language understanding Thoughts on Machine Learning in Natural Language Processing
B Favre – 2019 – hal-amu.archives-ouvertes.fr
… It is often stated that deep neural networks are not interpretable, but one can … and human-machine interactions, such as machine translation, summarization, speech recognition or dialog systems … assessment, predicting the quantity of silence required for a dialog system to take …

Populating the knowledge base of a conversational agent: human vs. machine
H Rodrigues, L Coheur, E Nyberg – Proceedings of the 34th ACM …, 2019 – dl.acm.org
… General and reference ? Evaluation; Metrics; • Comput- ing methodologies ? Natural language generation; Language resources … repre- sents the recent approaches based in deep neural networks, relying … WORK Many domain-oriented conversational agents (or chatbots in cur …

Ambient Assisted Living with Deep Learning
E Merdivan – 2019 – tel.archives-ouvertes.fr
… MPI Modified Policy Iteration NLG Natural Language Generation NLP Natural Language Processing … important components: improving activity recognition, addressing privacy concerns and developing intelligent dialogue systems for AAL systems, with an emphasis on a …

Privacy-Enabled Smart Home Framework with Voice assistant
S Hanke, E Sandner, L Chen, A Holzinger – researchgate.net
… user occupancy detection, privacy preserving data management and dialogue system for user … In deep neural networks, the features can be learned automatically instead of … Natural and Spoken Language Understanding (NLU or SLU), Natural Language Generation (NLG) and …

Impact of artificial intelligence on businesses: from research, innovation, market deployment to future shifts in business models
N Soni, EK Sharma, N Singh, A Kapoor – arXiv preprint arXiv:1905.02092, 2019 – arxiv.org
… Oracle, multinational computer technology corporation, “With regards to chatbots, which are in many ways the most recognizable form of AI, 80% of sales and … Product recommendation system: Tmall Smart Selection, AI-powered chatbot: Dian Xiaomi …

Linguistic Fundamentals for Natural Language Processing II: 100 Essentials from Semantics and Pragmatics
EM Bender, A Lascarides – Synthesis Lectures on Human …, 2019 – morganclaypool.com
… Spoken Dialogue Systems Kristiina Jokinen and Michael McTear 2009 … NLP), in the tasks of both Natural Language Understanding (NLU) and Natural Language Generation (NLG … This includes human- computer interactions (eg, search engines, chatbots, spoken user interfaces …

Novel Methods for Efficient Dialogue Policy Learning by Improving Agent-User Interaction
B Peng – 2019 – search.proquest.com
… tana 1 , Apple Siri 2 , Amazon Alexa 3 , Google Assistant 4 , etc The often said dialogue systems can improve user experience … ing, dialogue management, natural language generation, and speech synthesis [11, 12] … most critical component in a dialogue system. It usually has …

Multi-Agent Actor-Critic Reinforcement Learning for Argumentative Dialogue Systems
Y Yang – 2019 – academia.edu
… Over the last years, the combination of deep neural networks and reinforcement learning (ie deep reinforcement learning) has brought great success … 2.1.1 Dialogue Systems A dialogue system is a computer system intended to converse with human with a coherent structure [31 …

Unsupervised Text Representation Learning with Interactive Language
H Cheng – 2019 – digital.lib.washington.edu
… generation. Motivated by the success of neural network word embeddings, researchers have developed … neural-based spoken dialogue systems [36, 37] … Page 24. 13 tion scenario takes place between a human user and a dialogue system powered by conversational …

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
A Korhonen, D Traum, L Màrquez – … of the 57th Annual Meeting of the …, 2019 – aclweb.org
Page 1. ACL 2019 The 57th Annual Meeting of the Association for Computational Linguistics Proceedings of the Conference July 28 – August 2, 2019 Florence, Italy Page 2. Diamond Sponsors: Platinum Sponsors: ii Page 3. Gold sponsors: Keiosk Analytics Silver sponsors …

Automatically responding to customers
R Huijzer – pure.tue.nl
… One deviation from this definition is natural language generation, where text is generated … Conversational agent or dialogue systems aim to communicate with humans using natural lan- guage. Consensus is not clear on whether a chatbot is synonymous to conversational agent …

ArgueBot: Enabling debates through a hybrid retrieval-generation-based chatbot
I Kulatska – 2019 – essay.utwente.nl
… and Sentiment Analysis to classify whether the argument is for or against a given topic; Deep Neural Nets (DNNs … Chatbots can be broadly classified into generative which generate a response based on natural language generation techniques (Kim et al., 2018 … like the chatbot is …

Recommendation in Dialogue Systems
Y Sun – 2019 – escholarship.org
… These chatbots are implemented on different platforms, such as mobiles, home devices, and webpages. Dialogue systems are becoming indispensable tools in our life. First, the di … place an order. Second, the dialogue system is an important entrance of online …

Learning to Converse With Latent Actions
T Zhao – 2019 – lti.cs.cmu.edu
… Specifically, we first define the framework of latent action for dialog systems … demonstrates how explicit latent variable can be incorporated into deep neural natural language generation systems … of related research areas, including both work about dialog system and related …

Chatbots, will they ever be ready? Pragmatic shortcomings in communication with chatbots
S TONTS – 2019 – politesi.polimi.it
… trace back to the 1960s when a chat- bot called ELIZA was born in MIT labs and enabled humans to text with … teracting with chatbots. However, to the knowledge of the current work’s … pects of human-chatbot communica- tion to understand how they influence …

Cognitive Computing Recipes
A Masood, A Hashmi – Springer
Page 1. Cognitive Computing Recipes Artificial Intelligence Solutions Using Microsoft Cognitive Services and TensorFlow — Adnan Masood Adnan Hashmi Foreword by Matt Winkler Page 2. Cognitive Computing Recipes Artificial Intelligence Solutions Using …

A multimodal approach to sarcasm detection on social media
D Das – 2019 – researchgate.net
… Page 90 8.1 Sample positive and negative reviews, and replies from chatbot-based auto-replier sys- tem … Their proposed architecture consisted of a convolutional neural network (CNN), followed by a long short term memory (LSTM), and finally a deep neural network (DNN) …

Standardized representations and markup languages for multimodal interaction
R Tumuluri, D Dahl, F Paternò… – The Handbook of …, 2019 – dl.acm.org
… recognizers. Output processors Page 8. 354 Chapter 9 Standardized Representations and Markup Languages for Multimodal Interaction include natural language generation components and text-to-speech systems. Sev- eral …

How to Survive a Robot Invasion: Rights, Responsibility, and AI
DJ Gunkel – 2019 – books.google.com
… 3.2 AIML code for an ELIZA-type chatbot … significant portion of this work can be performed by a speech dialogue system (SDS), like … Now, however, Natural Language Generation (NLG) algorithms, like Narrative Science’s Quill and Automated Insights’ Wordsmith, can do that …