Notes:
A convolutional neural network (CNN) is a type of artificial neural network that is specifically designed to process data that has a grid-like structure, such as an image. CNNs are composed of multiple layers of interconnected nodes, which are inspired by the structure of the visual cortex in the human brain. Each layer in a CNN performs a specific function, such as detecting edges or other low-level features in the input data, and the layers are connected in a way that allows the network to gradually build up more complex representations of the input data.
One of the key advantages of CNNs is that they are able to automatically learn and extract features from the input data, without the need for manual feature engineering. This makes them well-suited to a wide range of tasks, including image classification, object detection, and image generation.
In the context of dialog systems, CNNs can be used to process and analyze the text input provided by users. For example, a CNN might be trained to identify specific keywords or phrases in the user’s input, or to identify the overall sentiment or emotion expressed in the text. This information can then be used by the dialog system to generate a more appropriate and relevant response.
Deep neural networks (DNNs) are artificial neural networks that have a large number of layers, typically consisting of hundreds or thousands of interconnected “neurons.” These layers are stacked on top of each other, and the output of one layer serves as the input for the next layer. DNNs are trained using large datasets and are able to learn and extract features from the data automatically, without the need for manual feature engineering.
Convolutional neural networks (CNNs) are a type of DNN that are particularly well-suited for image recognition and other tasks that involve processing and analyzing visual data. CNNs are inspired by the structure of the human visual system and are made up of a series of interconnected layers, including convolutional layers, pooling layers, and fully connected layers.
CNNs are typically used in combination with other types of layers in a DNN, and are often used as a feature extractor or preprocessor for other types of data. For example, a CNN might be used to extract features from an image, which could then be fed into a fully connected layer or another type of layer in a DNN for further processing and analysis.
Wikipedia:
See also:
100 Best Convolutional Neural Network Videos
From Eliza to XiaoIce: challenges and opportunities with social chatbots
HY Shum, X He, D Li – Frontiers of Information Technology & Electronic …, 2018 – Springer
… For this purpose, social chatbots must develop a set of skills to accommodate users’ requests … accomplishing tasks and ending the conversation as quickly as possible), social chat- bots take time … XiaoIce has been the most widely deployed so- cial chatbot since it was released …
Lingke: A fine-grained multi-turn chatbot for customer service
P Zhu, Z Zhang, J Li, Y Huang, H Zhao – arXiv preprint arXiv:1808.03430, 2018 – arxiv.org
… et al., 2015; Lowe et al., 2015) including TF-IDF, Convolutional Neural Network (CNN), Recurrent … A new architecture for multi-turn response selection in retrieval-based chatbots … Docchat: An information retrieval approach for chatbot engines using unstructured documents …
Chitty-Chitty-Chat Bot: Deep Learning for Conversational AI.
R Yan – IJCAI, 2018 – ijcai.org
… A standard chatbot system presumes that only humans will take the initiative role in chit-chats … at the entrance of future success in more advanced conversational systems (social chatbots and/or … Convolutional neural network architectures for matching natural language sentences …
Ethical challenges in data-driven dialogue systems
P Henderson, K Sinha, N Angelard-Gontier… – Proceedings of the …, 2018 – dl.acm.org
… In one such case, the Microsoft Tay Chatbot was taken offline after posting messages with blatant … Areas of Safety Concern Medical domains where chatbots have the potential to be used as … Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks …
Modeling multi-turn conversation with deep utterance aggregation
Z Zhang, J Li, P Zhu, H Zhao, G Liu – arXiv preprint arXiv:1806.09102, 2018 – arxiv.org
… Figure 2: Structure overview of the proposed dialogue system … The fourth module matches the response and each utterance at both word and utterance levels to feed a Convolutional Neural Network (CNN) for … Superagent: A customer service chatbot for e-commerce websites …
Troubling trends in machine learning scholarship
ZC Lipton, J Steinhardt – arXiv preprint arXiv:1807.03341, 2018 – arxiv.org
… We note that recent interest in chatbot startups co- occurred with anthropomorphic descriptions of dialogue systems and reinforcement learners both in papers and in the media, although it may be difficult to determine whether the lapses in scholarship caused the interest of …
Response ranking with deep matching networks and external knowledge in information-seeking conversation systems
L Yang, M Qiu, C Qu, J Guo, Y Zhang… – The 41st International …, 2018 – dl.acm.org
… M1 and M2 will be two input channels of a convolutional neural network (CNN) to learn important matching features, which will be aggregated by the final BiGRU layer and a multi-layer perceptron (MLP) to generate a matching score …
Empathetic Dialog Systems
P Fung, D Bertero, P Xu, JH Park, CS Wu… – The International …, 2018 – lrec-conf.org
… set of dialog poli- cies can be written to control how the chatbot response to … paper/talk, we propose that dialog systems, both task-oriented and chatbots, can benefit … We trained our emotional embeddings using Convolutional Neural Network and we projected the trained vectors …
Enhance word representation for out-of-vocabulary on ubuntu dialogue corpus
J Dong, J Huang – arXiv preprint arXiv:1802.02614, 2018 – arxiv.org
… One problem in chat-oriented human- machine dialog system is to reply a message within conversation contexts … The size of the corpus makes it attractive for the exploration of deep neural network modeling in the context of dialogue systems …
Learning Matching Models with Weak Supervision for Response Selection in Retrieval-based Chatbots
Y Wu, W Wu, Z Li, M Zhou – arXiv preprint arXiv:1805.02333, 2018 – arxiv.org
… simu- lates how we build a matching model in a retrieval- based chatbot: given {xi … Previous studies focus on architecture design for retrieval-based chatbots, but neglect the problems brought … Convolutional neural network archi- tectures for matching natural language sentences …
Style transfer in text: Exploration and evaluation
Z Fu, X Tan, N Peng, D Zhao, R Yan – Thirty-Second AAAI Conference on …, 2018 – aaai.org
… RUBER (Tao et al. 2017) was pro- posed to evaluate dialog system, it divides evaluation into referenced and unreferenced part … (Zhou et al. 2017) controls emotion of conversation, it also uses a classifier to evaluate chatbot gen- erated emotional response …
Alquist: The alexa prize socialbot
J Pichl, P Marek, J Konrád, M Matulík… – arXiv preprint arXiv …, 2018 – arxiv.org
… Eliza [2] is one of the most famous chat-bots from that era … Several methods of implementation of the open domain dialogue system have been proposed … The last approach uses a convolutional neural network (architecture described in section 5). The input to the neural network is …
Topic-based evaluation for conversational bots
F Guo, A Metallinou, C Khatri, A Raju… – arXiv preprint arXiv …, 2018 – arxiv.org
… coverage, can complement human judgment and uncover problems of a dialog system that are … with Recurrent Neural Networks (RNNs)[11], [20], [13] and Convolutional Neural Networks (CNNs) [9 … RER correlates well negatively with the user ratings for 15 chatbots (? = -0.717) …
DeepPavlov: Open-Source Library for Dialogue Systems
M Burtsev, A Seliverstov, R Airapetyan… – Proceedings of ACL …, 2018 – aclweb.org
… development of production-ready chatbots and complex … It can be a dialogue system that contains a goal-oriented and chatbot skills and chooses … The model im- plements neural network architecture based on shallow-and-wide Convolutional Neural Network 7https://catalog.ldc …
A reinforced topic-aware convolutional sequence-to-sequence model for abstractive text summarization
L Wang, J Yao, Y Tao, L Zhong, W Liu, Q Du – arXiv preprint arXiv …, 2018 – arxiv.org
… Compared to RNNs, convolutional neural networks (CNNs) enjoy several advan- tages, including the efficient training by leveraging parallel computing, and mitigating the … been introduced to a RNN-based sequence-to-sequence model [Xing et al., 2017] for chatbots to generate …
An auto-encoder matching model for learning utterance-level semantic dependency in dialogue generation
L Luo, J Xu, J Lin, Q Zeng, X Sun – arXiv preprint arXiv:1808.08795, 2018 – arxiv.org
… generation task is of great importance to many applications, ranging from open-domain chatbots (Higashinaka et al … Frames: a corpus for adding memory to goal-oriented dialogue systems … Convolutional neural network architec- tures for matching natural language sentences …
Multi-cast attention networks for retrieval-based question answering and response prediction
Y Tay, LA Tuan, SC Hui – arXiv preprint arXiv:1806.00778, 2018 – arxiv.org
… 1 INTRODUCTION Modeling textual relevance between document query pairs lives at the heart of information retrieval (IR) research. Intuitively, this enables a wide assortment of real life applications, ranging from standard web search to automated chatbots. The key idea is that …
Knowledge-aware Multimodal Dialogue Systems
L Liao, Y Ma, X He, R Hong, T Chua – 2018 ACM Multimedia Conference …, 2018 – dl.acm.org
… Also known as chat bots, the non-task- oriented systems converse with human typically … work will take a step forward to build knowledge-aware multimodal dialogue systems … such problems has been achieved through a combination of Convolutional Neural Networks (CNN) and …
Emotional dialogue generation using image-grounded language models
B Huber, D McDuff, C Brockett, M Galley… – Proceedings of the 2018 …, 2018 – dl.acm.org
… Mostafazadeh et al. [33] presented an approach for using low- level image inputs (ie, pixels) in order to ground a conversa- tional model. However, these convolutional neural network (CNN) features are high dimensional and difficult to interpret …
A survey on deep learning toolkits and libraries for intelligent user interfaces
J Zacharias, M Barz, D Sonntag – arXiv preprint arXiv:1803.04818, 2018 – arxiv.org
… Machine learning; Deep learning; Interactive machine learning; Hyper-parameter tuning; Convolutional neural networks … a chat is analogously available27 Four example chatbots are provided … for optimis- ing a visually grounded goal-directed dialogue system was implemented …
Intent Detection System Based on Word Embeddings
K Balodis, D Deksne – International Conference on Artificial Intelligence …, 2018 – Springer
… As a baseline we use the Wit.ai service 1 which is one of few popular chatbot creation services that supports Latvian language … Kim, Y.: Convolutional neural networks for sentence classification … Shawar, BA, Atwell, E.: Machine learning from dialogue corpora to generate chatbots …
Conversational memory network for emotion recognition in dyadic dialogue videos
D Hazarika, S Poria, A Zadeh, E Cambria… – Proceedings of the …, 2018 – aclweb.org
… Cambria et al., 2017), financial forecasting (Xing et al., 2018), and intelligent systems such as smart homes and chat- bots (Young et al … 4.1.1 Textual Features Extraction We extract features from the transcript of an ut- terance video using convolutional neural networks (CNNs) …
Convolutional neural networks for dialogue state tracking without pre-trained word vectors or semantic dictionaries
M Korpusik, J Glass – Proceedings of 2018 IEEE Spoken …, 2018 – groups.csail.mit.edu
… Index Terms— Convolutional Neural Networks, Dia- logue State Tracking, Word Vectors, Semantic Dictionaries … These systems can be divided into two categories: chatbots that simply entertain … user through fun conver- sation, and task-oriented dialogue systems that accomplish …
Memory-based matching models for multi-turn response selection in retrieval-based chatbots
X Lu, M Lan, Y Wu – CCF International Conference on Natural Language …, 2018 – Springer
… 4, 5, 6, 7]. Compared to generation-based chatbots, retrieval-based chatbots enjoy the … utterance in the context on multiple levels of granularity with a convolutional neural network, and then … the top-ranked systems provided by organizers for this Retrieval Dialogue System task …
Smarter Response with Proactive Suggestion: A New Generative Neural Conversation Paradigm.
R Yan, D Zhao – IJCAI, 2018 – ijcai.org
… on research in the generation-based human-computer conversa- tional system in the open domain, known as non-task-oriented “chatbots” [Yan et al., 2016a; 2016b]. For all these years, people have formulated a well-defined paradigm for mainstream chatbot systems …
Content-Oriented User Modeling for Personalized Response Ranking in Chatbots
B Liu, Z Xu, C Sun, B Wang, X Wang, DF Wong… – IEEE/ACM Transactions …, 2018 – dl.acm.org
… implicitly based on user generated contents, and it is promising to perform as an important component in chatbots to select the personalized responses for each user. Index Terms—User modeling, personalization, response rank- ing, conversational model, personalized chatbot …
Measuring semantic coherence of a conversation
S Vakulenko, M de Rijke, M Cochez… – International Semantic …, 2018 – Springer
… References. 1. Athreya, RG, Ngonga, A., Usbeck, R.: Enhancing community interactions with data-driven chatbots – the DBpedia chatbot. In: WWW 2018 Companion … 1289–1292 (2017)Google Scholar. 14. Kim, Y.: Convolutional neural networks for sentence classification …
Coupled context modeling for deep chit-chat: towards conversations between human and computer
R Yan, D Zhao – Proceedings of the 24th ACM SIGKDD International …, 2018 – dl.acm.org
… most hardcore problems in computer science. Conversational systems are of growing importance due to their promising potentials and com- mercial values as virtual assistants and chatbots. To build such systems with adequate …
Building Advanced Dialogue Managers for Goal-Oriented Dialogue Systems
V Ilievski – arXiv preprint arXiv:1806.00780, 2018 – arxiv.org
… Page 3. Abstract Goal-Oriented (GO) Dialogue Systems, colloquially known as goal oriented chat- bots, help users achieve a predefined goal (eg book a movie ticket) within a closed domain … Thus, most of the chatbot research is on the closed-domain Chatbots, which is a …
Learn what not to learn: Action elimination with deep reinforcement learning
T Zahavy, M Haroush, N Merlis… – Advances in Neural …, 2018 – papers.nips.cc
… Wu et al., 2016), travel planners (Peng et al., 2017), restaurant/hotel bookers (Budzianowski et al., 2017), chat-bots (Serban et al … DRL approach with two DNNs, a DQN and an Action Elimination Network (AEN), both designed using a Convolutional Neural Network (CNN) that is …
Deep Learning in Spoken and Text-Based Dialog Systems
A Celikyilmaz, L Deng, D Hakkani-Tür – Deep Learning in Natural …, 2018 – Springer
… For instance Xu and Sarikaya (2013) extracted features using convolutional neural networks to feed into a CRF model … Chatterbot, 19 a conversational dialog engine for creating chat bots, chatbot-rnn, 20 a toy chatbot powered by … In metaguide.com, 21 top 100 chatbots are listed …
Artificial intelligence in the rising wave of deep learning: the historical path and future outlook [perspectives]
L Deng – IEEE Signal Processing Magazine, 2018 – ieeexplore.ieee.org
… Due to this strength, the first-generation AI systems are still in use today. Examples are nar- row-domain dialogue systems and chat- bots, chess-playing programs, traffic light controllers, optimization software for logistics of good deliveries, etc …
Response selection of multi-turn conversation with deep neural networks
Y Wang, Z Yan, Z Li, W Chao – CCF International Conference on Natural …, 2018 – Springer
… Scholar. 2. Hu, B., Lu, Z., Li, H., Chen, Q.: Convolutional neural network architectures for … ubuntu dialogue corpus: a large dataset for research in unstructured multi-turn dialogue systems … network: a new architecture for multi-turn response selection in retrieval-based chatbots …
Image inspired poetry generation in xiaoice
WF Cheng, CC Wu, R Song, J Fu, X Xie… – arXiv preprint arXiv …, 2018 – arxiv.org
… We try both in our experiments. 3.2 Keyword Extraction We propose detecting objects and sentiments from each im- age with two parallel convolutional neural networks (CNN), which share the same network architecture but with differ- ent parameters …
Improving Matching Models with Contextualized Word Representations for Multi-turn Response Selection in Retrieval-based Chatbots
C Tao, W Wu, C Xu, Y Feng, D Zhao, R Yan – arXiv preprint arXiv …, 2018 – arxiv.org
… While previous research focuses on build- ing task-oriented dialog systems (Young et al., 2010) that … more re- cent attention is drawn to developing non-task ori- ented chatbots which can … Existing approaches to building a chatbot include generation-based meth- ods (Shang et al …
Efficient purely convolutional text encoding
S Malik, A Lancucki, J Chorowski – arXiv preprint arXiv:1808.01160, 2018 – arxiv.org
… Such approaches have been applied by participants of recent chat- bot contests: The 2017 Alexa Prize … Convolutional neural networks have the inherent ability to detect local structures in the data … using a method similar to the one used in Poetwannabe chatbot [Chorowski et al …
Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce
M Qiu, L Yang, F Ji, W Zhao, W Zhou, J Huang… – arXiv preprint arXiv …, 2018 – arxiv.org
… To form our data set, we concatenated utterances within three turns 7 to form a query, and used the chat- bot system to call back top 15 most similar … Alime chat: A sequence to sequence and rerank based chatbot engine … Convolutional neural network for paraphrase identification …
Multi-Cast Attention Networks
Y Tay, LA Tuan, SC Hui – Proceedings of the 24th ACM SIGKDD …, 2018 – dl.acm.org
… 1 INTRODUCTION Modeling textual relevance between document query pairs lives at the heart of information retrieval (IR) research. Intuitively, this enables a wide assortment of real life applications, ranging from standard web search to automated chatbots …
Improving human-computer interaction in lowresource settings with text-to-phonetic data augmentation
A Stiff, P Serai, E Fosler-Lussier – Submitted to 2019 IEEE International … – cse.ohio-state.edu
… Index Terms— Low-resource, spoken dialog systems, chatbot 1. INTRODUCTION … [3] Madelaine Plauché, ¨Ozgür etin, and Udhaykumar Nal- lasamy, “How to build a spoken dialog system with lim- ited ( or … 1, pp. I–573. [5] Yoon Kim, “Convolutional neural networks for sentence …
Evaluation of real-time deep learning turn-taking models for multiple dialogue scenarios
D Lala, K Inoue, T Kawahara – Proceedings of the 2018 on International …, 2018 – dl.acm.org
… a question-answering system and a conversational chatbot. Turn-taking is of particular importance to humanoid robots, because users will expect them to behave similar to a real human. However, human-like natural turn-taking is still a long way off in spoken dialogue systems …
Aspect-based question generation
W Hu, B Liu, J Ma, D Zhao, R Yan – 2018 – openreview.net
… Duan et al., 2017). Since questioning is an important communication skill, question generation plays an important role in both general-purpose chatbot systems and goal-oriented dialogue systems. In the context of dialogue …
Towards Building Large Scale Multimodal Domain-Aware Conversation Systems
A Saha, MM Khapra, K Sankaranarayanan – Thirty-Second AAAI …, 2018 – aaai.org
… However, even though there is a growing demand for chat- bots that can converse using multiple modalities with hu- mans in … using a 4096 di- mensional representation obtained from the FC6 layer of a VGGNet-16 (Simonyan and Zisserman 2014) convolutional neural network …
Data-Driven Language Understanding for Spoken Dialogue Systems
N Mrkši? – 2018 – repository.cam.ac.uk
… information (Henderson et al., 2014b).2 1This term is used interchangeably with goal-oriented dialogue systems. 2Alternative chat-bot style systems do not make use of task ontologies or the pipeline model. Instead, these models …
Automated Scoring of Chatbot Responses in Conversational Dialogue
SK Yuwono, W Biao, LF D’Haro – pdfs.semanticscholar.org
… In: WOCHAT: Workshop on Chatbots and Conversational Agent Technolo- gies (2016) 3. Banchs … Automated Scoring of Chatbot Responses in Conversational Dialogue … TS: Look and think twice: Capturing top-down visual attention with feedback convolutional neural networks …
A View of the State of the Art of Dialogue Systems
L Ozaeta, M Graña – … Conference on Hybrid Artificial Intelligence Systems, 2018 – Springer
… first conversational system, ELIZA, considered one of the most important chatbot dialogue systems in the … Learning, MN Memory Networks, RL Reinforcement Learning, CNN Convolutional Neural Network, LSTM Long … 9]. Most either rule-based or corpus-based chatbots tend to …
A Survey on Chatbot Implementation in Customer Service Industry through Deep Neural Networks
M Nuruzzaman, OK Hussain – 2018 IEEE 15th International …, 2018 – ieeexplore.ieee.org
… as recurrent neural networks (RNN), deep neural networks (DNN) and convolutional neural networks (CNN) … the more challenging issues that are normally obstacles for simpler chatbots … Chatbot Technical Specification Drawback Input/output Technique Eliza [9] Basic Pattern …
Generative Indonesian Conversation Model using Recurrent Neural Network with Attention Mechanism
A Chowanda, AD Chowanda – Procedia Computer Science, 2018 – Elsevier
… for computer, the details techniques to build conversation models for agents be they chatbots or Embodied … 9,10,11,4 in NLP, and virtual agents (ECA or chatbot) have implemented … can be used in most of deep learning algorithm such as Convolutional Neural Network (CNN)23 …
Two-Step Training and Mixed Encoding-Decoding for Implementing a Generative Chatbot with a Small Dialogue Corpus
J Kim, HG Lee, H Kim, Y Lee, YG Kim – Proceedings of the Workshop on …, 2018 – aclweb.org
… is split into syllable sequences, and is merged into an embed- ding vector using a convolutional neural network (Kim et al … with input queries when a dialogue corpus is not sufficient to train chatbots based on … Alime chat: A sequence to sequence and rerank based chatbot engine …
Isa: Intuit Smart Agent, A Neural-Based Agent-Assist Chatbot
Z Xue, TY Ko, N Yuchen, MKD Wu… – 2018 IEEE International …, 2018 – ieeexplore.ieee.org
… We show that a task-specific chatbot suits our needs and propose a solution … Montani, Ines, ”spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental … ”Chatbots and the new world of HCI.” interactions 24.4 (2017): 38-42 …
Lead Engagement by Automated Real Estate Chatbot
T Quan, T Trinh, D Ngo, H Pham… – … on Information and …, 2018 – ieeexplore.ieee.org
… In [6,7], Convolutional Neural Network (CNN) is used to classify intent and domain of the … Even though chatbots cannot fully replace the traditional relation between agents and home buyers … V. EXPERIMENTS The accuracy of our chatbot heavily relies on its capability of intent …
Conversation Modeling with Neural Network
JY Patil, GP Potdar – Asian Journal of Research in Computer …, 2018 – journalajrcos.com
… Keywords: Natural language processing; deep learning; Chatbots; natural language understanding; artificial … char-CNN-txt- classify[14] evaluated deep Convolutional Neural Network (CNN) on large … How not to evaluate your dialogue system: An empirical study of unsupervised …
Implementation of A Neural Natural Language Understanding Component for Arabic Dialogue Systems
AM Bashir, A Hassan, B Rosman, D Duma… – Procedia computer …, 2018 – Elsevier
… 25 [7] Lee JY, Dernoncourt F. Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks … 10] Moubaiddin A, Shalbak O, Hammo B, Obeid N. Arabic Dialogue System for Hotel … North Dakota [12] Abu Ali D, Habash N. Botta: An Arabic Dialect Chatbot …
A Manually Annotated Chinese Corpus for Non-task-oriented Dialogue Systems
J Li, Y Song, H Zhang, S Shi – arXiv preprint arXiv:1805.05542, 2018 – arxiv.org
… size fits all” replies, which is a major drawback of the existing chatbots (Li et al … to as- sume all user-generated responses to be positive in chatbot training and … models, bidirectional long short-term memory (BiLSTM) (Tan et al., 2015) and convolutional neural networks (CNN) (Sev …
Spatio-Temporal Matching Network for Multi-Turn Responses Selection in Retrieval-Based Chatbots
J Lu, Z Xie, G Ling, C Zhou, Z Xu – workshop.colips.org
Page 1. Spatio-Temporal Matching Network for Multi-Turn Responses Selection in Retrieval-Based Chatbots … Banchs, RE, and Li, H. 2012. Iris: a chat-oriented dialogue system based on the vector space model … 3d convolutional neural networks for human action recognition …
Visual Dialog with Multi-turn Attentional Memory Network
D Kong, F Wu – Pacific Rim Conference on Multimedia, 2018 – Springer
… Conversation Model and Chatbots … performs better in all evaluation metrics, this proves the importance of information transition in dialog systems and the … Noh, H., Seo, PH, Han, B.: Image question answering using convolutional neural network with dynamic parameter prediction …
Open-domain neural conversational agents: The step towards artificial general intelligence
S Arsovski, S Wong, AD Cheok – International Journal of …, 2018 – openaccess.city.ac.uk
… After almost 50 years since the introduction of chatbots and numerous surveys over the … by applying deep reinforcement learning to model future reward in chatbot dialogue … Authors in [29] leverages convolutional neural network (CNN) and Recurrent Neural Network (RNN) for …
Chatbol, a chatbot for the Spanish “La Liga”
C Segura, A Palau, J Luque, M Costa-jussa, R Banchs – 2018 – oar.a-star.edu.sg
… During all these years, chatbots have been approached from different perspectives which … spacy 2: Natural language understanding with bloom embeddings, convolutional neural networks and incremental parsing … Moocbuddy: a chatbot for personalized learning with moocs …
Advanced Social Interaction with Agents: 8th International Workshop on Spoken Dialog Systems
M Eskenazi, L Devillers, J Mariani – 2018 – Springer
… These systems can be characterized as combining more than one slot-filling or chatbot system … Both models deploy a convolutional neural network … Page 19. Contents Part I Chatbots and Conversational Agents Building Rapport with Extraverted and Introverted Agents …
The Design and Implementation of XiaoIce, an Empathetic Social Chatbot
L Zhou, J Gao, D Li, HY Shum – arXiv preprint arXiv:1812.08989, 2018 – arxiv.org
… The design objective of the chatbot is to find optimal policies and skills to … in the conversational AI community [4]. Its role in developing social chatbots is becoming … represents the overall semantic information of the image, using deep convolutional neural networks (CNNs), as …
Implementing ChatBots using Neural Machine Translation techniques
A Nuez Ezquerra – 2018 – upcommons.upc.edu
… First, this section defines the area which studies and develops chatbot models, Natural … Chatbots belong to the area of NLP given the importance of their ability to … the problem, some architectures are preferred over the others, for instance, convolutional neural networks for image …
Cyberphysical strategies to develop creative interaction between students and social robots
M Graña – The Proceedings of JSME annual Conference on …, 2018 – jstage.jst.go.jp
… since the first conversational system, ELIZA, considered one of the most important chatbot dialog systems in the … vocabulary continuous speech recognition of Deep Neural Networks (DNNs), such as Convolutional Neural Networks (CNNs) and Long … Dialog systems and chatbots …
DLCEncDec: A Fully Character-Level Encoder-Decoder Model for Neural Responding Conversation
S Wu, Y Li, X Zhang, Z Wu – 2018 IEEE 42nd Annual Computer …, 2018 – ieeexplore.ieee.org
… witnessed a surge of interest in building conversation systems such as smart agents or chatbots … a convolutional neural network (CNN) [13] and a highway network [22] on the top of character … Chen, X. Liu, D. Yin, and J. Tang, “A Survey on Dialogue Systems: Recent Advances …
Fantom: A Crowdsourced Social Chatbot using an Evolving Dialog Graph
P Jonell, M Bystedt, FI Dogan, P Fallgren, J Ivarsson… – dex-microsites-prod.s3.amazonaws …
… This paper presents Fantom, a social chatbot that has taken part in the second installment of … The main problem in designing conversational chatbots is that two of the major design goals … There are several important distinctions to make when it comes to modern dialog systems …
End-to-End Task-Oriented Dialogue System with Distantly Supervised Knowledge Base Retriever
L Qin, Y Liu, W Che, H Wen, T Liu – Chinese Computational Linguistics …, 2018 – Springer
… used in past literature for evaluating dialogue systems both of the chatbot and task … Wen, TH, et al.: A network-based end-to-end trainable task-oriented dialogue system … K., Chen, Y., Zhao, J.: Distant supervision for relation extraction via piecewise convolutional neural networks …
Chat Discrimination for Intelligent Conversational Agents with a Hybrid CNN-LMTGRU Network
DS Moirangthem, M Lee – Proceedings of The Third Workshop on …, 2018 – aclweb.org
… Recently, intelligent dialog systems and smart assistants have attracted the atten- tion of many, and development of novel dialogue agents … We introduce a hybrid of convolutional neural network (CNN) and a lateral multiple timescale gated recurrent units (LMTGRU) that can …
EmotionPush: Emotion and Response Time Prediction Towards Human-Like Chatbots
CY Huang, LW Ku – 2018 IEEE Global Communications …, 2018 – ieeexplore.ieee.org
… are selected as Readers, as if playing the role of users in a chat scenario with the chatbot; more relevant … with the hope to see the EmotionPush dataset used as a good open benchmark for training human-like chatbots … [29] Y. Kim, “Convolutional neural networks for sentence …
Ruuh: A Deep Learning Based Conversational Social Agent
S Damani, N Raviprakash, U Gupta… – arXiv preprint arXiv …, 2018 – arxiv.org
… Dialogue systems and conversational agents are becoming increasingly popular in the modern society but … Lessons from Building a Large-scale Commercial IR-based Chatbot for an Emerging Market … Emulating human conversations using convolutional neural network-based IR …
Memory-Based Model with Multiple Attentions for Multi-turn Response Selection
X Lu, M Lan, Y Wu – International Conference on Neural Information …, 2018 – Springer
… Among them, SMN uses the convolutional neural network (CNN) [6] to match each utterance in … the rapid growth of social conversation data on Internet, building a chatbot on open … a response with natural language generation techniques and retrieval-based chatbots select a …
Markov Chain Neural Networks
M Awiszus, B Rosenhahn – Proceedings of the IEEE …, 2018 – openaccess.thecvf.com
… a foreseeable reaction given a specific game configuration or (b) always to the same answer for a given comment in a dialog system … This allows the synthesis of new text blocks, eg useful for artificial chat-bots … Im- agenet classification with deep convolutional neural networks …
Region Based Robust Facial Expression Analysis
Z Lian, Y Li, J Tao, J Huang… – 2018 First Asian …, 2018 – ieeexplore.ieee.org
… which have wild applications such as the movie booking [1], chatbots [2] and … to recognize facial expression based on regions of interest, which guides convolutional neural networks (CNNs) to … D. Suhubdy, and S. Zhang, “A Deep Reinforcement Learning Chatbot,” CoRR, 2017 …
Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory
D Cai, Y Wang, V Bi, Z Tu, X Liu, W Lam… – arXiv preprint arXiv …, 2018 – arxiv.org
… Introduction This paper focuses on tackling the challenges to develop a chit-chat style dialogue system (also known as chatbot). Chi- chat style dialogue system aims at giving meaningful and coherent responses given a dialogue query in open domain …
Proceedings of the 22nd Conference on Computational Natural Language Learning
A Korhonen, I Titov – Proceedings of the 22nd Conference on …, 2018 – aclweb.org
… Model for Low-Resource Natural Language Generation in Dialogue Systems Van-Khanh Tran … Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction Maha … Churn Intent Detection in Multilingual Chatbot Conversations and Social Media …
Topic-Net Conversation Model
M Peng, D Chen, Q Xie, Y Zhang, H Wang… – … Conference on Web …, 2018 – Springer
… Conversation systems, also known as dialogue systems and sometimes chatbots, aim at generating relevant … this paper, we study the response generation problem of open-domain chatbots … The topic generation model is a convolutional neural network to obtain the topic words …
Alquist 2.0: Alexa Prize Socialbot Based on Sub-Dialogue Models
J Pichl, CTU FEE, P Marek, J Konrád, M Matulík… – dex-microsites-prod.s3.amazonaws …
… Firstly, we use input convolutional neural network described in [9] with pretrained fastText embeddings [14 … It is used to test end-to-end dialog systems in a way that favors … because it is the only task which contains records of real-world conversations between humans and chatbot …
Response selection from unstructured documents for human-computer conversation systems
Z Yan, N Duan, J Bao, P Chen, M Zhou, Z Li – Knowledge-Based Systems, 2018 – Elsevier
… Side-by-side evaluation between DocChat and a famous chatbot demonstrates that DocChat performs better on domain related queries … ARC-I and ARC-II [20] propose a convolutional neural network (CNN) based method to align sentences pairs word by word. Ji et al …
Artificial Intelligence and Natural Language
A Filchenkov, L Pivovarova, J Žižka – 2018 – Springer
… and Andrey Filchenkov Boosting a Rule-Based Chatbot Using Statistics … Deep Learning for Acoustic Addressee Detection in Spoken Dialogue Systems … 277 Dmitry Kravchenko Character-Level Convolutional Neural Network for Paraphrase Detection and …
Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize
C Khatri, B Hedayatnia, A Venkatesh, J Nunn… – arXiv preprint arXiv …, 2018 – arxiv.org
… Iris also trained an entity classifier, using DBpedia (Auer et al., 2007) entities as their dataset and a convolutional neural network. Anaphora and Co-reference Resolution: Multi-turn dialog systems need to resolve ambiguous phrases with reference to prior entities for …
A review on data fusion methods in multimodal human computer dialog
M YANG, J TAO – vr-ih.com
… The role of human-computer dialogue system has gradually changed from the role of service to the role of chat partner … RNN, and convolutional neural network (CNN) based end-to-end encoder-decoder model have been widely used in Non-Task-Oriented multi-modal human …
Using Deep Learning and an External Knowledge Base to Develop Human-Robot Dialogues
JY Huang, TA Lin, WP Lee – 2018 IEEE International …, 2018 – ieeexplore.ieee.org
… Recently, the demand on chatbots has begun to perform open-domain conversations … DEVELOPING AHUMAN-ROBOT DIALOGUE SYSTEM … of deep learning neural networks, a LSTM (long short-term memory) model (in fact, LSTM with a Convolutional neural network as shown …
Question Answering for Technical Customer Support
Y Li, Q Miao, J Geng, C Alt, R Schwarzenberg… – … Conference on Natural …, 2018 – Springer
… 3. Lowe, RT, Pow, N., Serban, IV: Training end-to-end dialogue systems with the … Hu, B., Lu, Z., Li, H.: Convolutional neural network architectures for matching natural language sentences … network: a new architecture for multi-turn response selection in retrieval-based Chatbots …
“I think it might help if we multiply, and not add”: Detecting Indirectness in Conversation
P Goel, Y Matsuyama, M Madaio, J Cassell – articulab.hcii.cs.cmu.edu
… in interpersonal communication, incorporating its detection in spoken dialogue systems may ultimately … Lee, JY, Dernoncourt, F.: Sequential short-text classification with recurrent and convolutional neural networks … Reynolds, M.: Chatbots learn how to drive a hard bargain (2017 …
Learning Task-Oriented Dialog with Neural Network Methods
B Liu – 2018 – bingliu.me
… Dialog systems, also known as conversational agents or chatbots, are playing an increasingly … Voice Output Figure 2.1: Pipeline architecture for task-oriented spoken dialog systems … classification include using recursive neural network [41], convolutional neural network [45 …
Concorde: Morphological Agreement in Conversational Models
D Polykovskiy, D Soloviev… – Asian Conference on …, 2018 – proceedings.mlr.press
… Abstract Neural conversational models are widely used in applications such as personal assistants and chat bots … 1. Introduction Conversational models appear in a wide range of applications, from simple rule-based chatbots to complex personal assistants …
Creating an Emotion Responsive Dialogue System
A Vadehra – 2018 – uwspace.uwaterloo.ca
… used for transforming the dimension of inputs and outputs of different complex models like convolutional neural network (CNN) and … Models that can generate utterances in response to an input utterance like dialogue system, conversational agent and chatbots have been …
Dialog manager for conversational AI
BP Marek – 2018 – core.ac.uk
… 26 4.3 Hybrid code networks with convolutional neural network … Chapter 1 Introduction Personal voice assistants and text chatbots are newly emerging types of user interface. Their … ligence. The dialogue manager is the main part of the dialogue system, which communicates …
Adversarial Learning for Chinese NER from Crowd Annotations
YS Yang, M Zhang, W Chen, W Zhang, H Wang… – Thirty-Second AAAI …, 2018 – aaai.org
… Identifying these entities is useful for chatbot and e-commerce platforms (Klüwer 2011) … CNN Following, we add a convolutional neural network (CNN) module based on the concatenated outputs of the common Bi-LSTM and the label Bi-LSTM, to produce the final fea- tures for …
Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy
L Rojas-Barahona, BH Tseng, Y Dai… – arXiv preprint arXiv …, 2018 – arxiv.org
… The first model involves a convolutional neural network (CNN) op- erating over distributed words representations … since the first time researchers at- tempted to build a dialogue system (Weizenbaum, 1966) … The Stanford Woebot chat-bot proposed by (Fitz- patrick et al., 2017) is …
A Knowledge-Grounded Multimodal Search-Based Conversational Agent
S Agarwal, O Dusek, I Konstas, V Rieser – arXiv preprint arXiv:1810.11954, 2018 – arxiv.org
… become ubiquitous, with variants ranging from open-domain conversa- tional chit-chat bots (Ram et … Traditional goal-oriented dialogue systems relied on slot-filling approach to this task, ie … GRU cells (Cho et al., 2014) and genc ? is a Convolutional Neural Network (CNN) image …
Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy
LMR Barahona, BH Tseng, Y Dai, C Mansfield… – Proceedings of the …, 2018 – aclweb.org
… The first model involves a convolutional neural network (CNN) op- erating over distributed words representations … since the first time researchers at- tempted to build a dialogue system (Weizenbaum, 1966) … The Stanford Woebot chat-bot proposed by (Fitz- patrick et al., 2017) is …
Laughbot: Detecting Humor in Spoken Language with Language and Audio Cues
K Park, A Hu, N Muenster – Future of Information and Communication …, 2018 – Springer
… Chatbots Spoken natural language processing Deep learning Machine learning. Download conference paper … and language features were fed into three models: convolutional neural network (CNN), RNN … our research and testing, predicting humor using a chatbot-style audio …
AIA. Artificial intelligence for art
R Lisek – Editorial Coordinators: Rufus Adebayo, Ismail Farouk …, 2018 – researchgate.net
… the above approaches through introducing text into the game, building chatbots or intelligent … A., Sutskever, I., and Hinton, G. Imagenet classification with deep convolutional neural networks … of the 24th International Symposium on Electronic Art Dialogue Systems: A Data-driven …
Language Style Transfer from Non-Parallel Text with Arbitrary Styles
Y Zhao, VW Bi, D Cai, X Liu, K Tu, S Shi – 2018 – openreview.net
… A simple framework for this task is to first use human dialog data to train a chatbot system, such as a retrieval-based dialog model (Lowe et al., 2015), and then transfer the output responses with a language style transfer … (2016) used the Convolutional Neural Networks (CNNs) to …
Activity Recognition: Translation across Sensor Modalities Using Deep Learning
T Okita, S Inoue – Proceedings of the 2018 ACM International Joint …, 2018 – dl.acm.org
… We take an approach of convolutional neural network to- wards MIL[20, 19] … For example, a chatbot is a conversational agent which exchanges words with human being … We develop a chat- bot for telemedicine which identifies the possible diseases for patients[8]. Combined with …
Computational Analysis and Understanding of Natural Languages: Principles, Methods and Applications
CR Rao, VN Gudivada – 2018 – books.google.com
… 6.6 Encoder-Decoder Sequence-to-Sequence Architectures 6.7 Recursive Neural Networks Convolutional Neural Networks 7.1 What … language generation, semantic analysis, grammar correction, question- answering systems, spoken dialog systems, chatbots, passage retrieval …
Topic-Based Question Generation
W Hu, B Liu, R Yan, D Zhao, J Ma – 2018 – openreview.net
… Since questioning is an important communication skill, question generation plays an important role in both general-purpose chatbot systems and goal-oriented dialogue systems … Yoon Kim. Convolutional neural networks for sentence classification. In EMNLP, 2014 …
Resolving Abstract Anaphora Implicitly in Conversational Assistants using a Hierarchically stacked RNN
P Khurana, P Agarwal, G Shroff, L Vig – Proceedings of the 24th ACM …, 2018 – dl.acm.org
… com ABSTRACT Recent proliferation of conversational systems has resulted in an increased demand for more natural dialogue systems, capable of more sophisticated interactions than merely pro- viding factual answers. This …
Unsupervised Dialogue Act Classification with Optimum-Path Forest
LCF Ribeiro, JP Papa – 2018 31st SIBGRAPI Conference on …, 2018 – ieeexplore.ieee.org
… for other tasks, such as the development of chatbots where the … mechanism; Lee and Dernoncourt [29] obtained 84.6% using Convolutional Neural Networks (CNN) with … E. Dialog System Technology Challenges datasets The Dialog System Technology Challenges, previously Di …
Specifying and Implementing Multi-Party Conversation Rules with Finite-State-Automata
MG de Bayser, MA Guerra, P Cavalin… – Workshops at the Thirty …, 2018 – aaai.org
… The pro- posed architecture is based on recurrent convolutional neural networks (RCNN) with shared feature … to always have precedence over prohibitions in finch, both chatbots replied to … and getting ready to run user experience experiments with both chatbot developers and …
Reinforcement Learning and Robotics
A Vieira, B Ribeiro – Introduction to Deep Learning Business Applications …, 2018 – Springer
… This allowed the training of a deep convolutional neural network (CNN) for the task of predicting grasp locations … building a beta and collecting clinical data, Woebot Labs just launched the full commercial product—a cheeky, personalized chatbot that checks … 6.7 News Chatbots …
KNADIA: Enterprise KNowledge Assisted DIAlogue Systems Using Deep Learning
M Singh, P Agarwal, A Chaudhary… – 2018 IEEE 34th …, 2018 – ieeexplore.ieee.org
… The NADIA natural dialogue system [14] (note that this prior work has no relation to ours, despite the similarity of its name) makes the process of dia- logue design easy by eliminating the need of coding … [1] proposed deep reinforcement learning based chatbot MILABOT which …
Response generation by context-aware prototype editing
Y Wu, F Wei, S Huang, Z Li, M Zhou – arXiv preprint arXiv:1806.07042, 2018 – arxiv.org
… various aspects. 1 Introduction In recent years, non-task oriented chatbots, fo- cused on responding to humans intelligently on a variety of topics, have drawn much attention from both academia and industry. Existing approaches …
Deep Context Resolution
J Chen – 2018 – uwspace.uwaterloo.ca
… iii Page 4. Abstract Conversations depend on information from the context. To go beyond one-round con- versation, a chatbot must resolve contextual information such as: 1) co-reference resolution, 2) ellipsis resolution, and 3) conjunctive relationship resolution …
Towards effective AI-powered agile project management
HK Dam, T Tran, J Grundy, A Ghose… – arXiv preprint arXiv …, 2018 – arxiv.org
… word2vec, paragraph2vec, Long Short-Term Memory (used in Google Translate), or Convolutional Neural Networks (used in … The chatbot can be asked different types of questions, such as “Show me … Future chatbots can be trained end-to-end [14] and person- specific instead of …
Sentiment Classification on Erroneous ASR Transcripts: A Multi View Learning Approach
SH Dumpala, I Sheikh, R Chakraborty… – 2018 IEEE Spoken …, 2018 – ieeexplore.ieee.org
… utterances and documents has been discussed in con- text of chat bots [24] and … Real- time speech emotion and sentiment recognition for in- teractive dialogue systems,” in Proceedings … and HY Lee, “Mitigating the impact of speech recognition errors on chatbot using sequence …
Group Cognition and Collaborative AI
J Koch, A Oulasvirta – Human and Machine Learning, 2018 – Springer
… Examples include interactive health interfaces [33, 71] and industrial workflows [39], along with dialogue systems such as chat bots [41] and … They used a convolutional neural network to analyse image features and trained a separate recurrent neural network to generate …
LSDSCC: A Large Scale Domain-Specific Conversational Corpus for Response Generation with Diversity Oriented Evaluation Metrics
Z Xu, N Jiang, B Liu, W Rong, B Wu, B Wang… – Proceedings of the …, 2018 – aclweb.org
… 1 Introduction Conversational agents (aka Chat-bots) are ef- fective media to establish communications with human beings and have received much attention from academic and industrial experts in recent years (Serban et al., 2017) …
SlugBot: Developing a Computational Model and Framework of a Novel Dialogue Genre
KK Bowden, J Wu, W Cui, J Juraska, V Harrison… – dex-microsites-prod.s3.amazonaws …
… Discourse coherence in SCRIPT MODEL dialogue systems is created by the user interaction … Other existing retrieval based chatbots also operate on large existing corpora such as … uses a combination of Recurrent Neural Network and Convolutional Neural Network architectures …
Response Selection and Automatic Message-Response Expansion in Retrieval-Based QA Systems using Semantic Dependency Pair Model
MH Su, CH Wu, KY Huang, WH Lin – ACM Transactions on Asian and …, 2018 – dl.acm.org
… alignment between the post and the response [8]. The retrieval-based chatbots choose a … methods, and it is difficult to develop an extensible open-domain chatbot system … a statistical language generator based on a joint recurrent and convolutional neural network struc- ture and …
Visual questioning agents
U Jain – 2018 – ideals.illinois.edu
… more literal while others are more inferential. Diversity in VQG is essential to initiate or continue an engaging conversation. This helps AI systems such as driving assistants, chatbots, etc., to perform better on Turing tests. An AI sys …
Emory IrisBot: An Open-Domain Conversational Bot for Personalized Information Access
A Ahmadvand, IJ Choi, H Sahijwani, J Schmidt, M Sun… – dex-microsites-prod.s3.amazonaws …
… 3.1.1 Semantic and Lexical Classification using Convolutional Neural Networks (CNN … a fallback, for when classifier failed or components couldn’t find a good enough response, we had a general catch-all Chat component, which was based on the ALICE chatbot, implemented in …
Gunrock: Building A Human-Like Social Bot By Leveraging Large Scale Real User Data
CY Chen, D Yu, W Wen, YM Yang, J Zhang, M Zhou… – dex-microsites-prod.s3.amazonaws …
… In comparison, social chatbots require in-depth communication skills with emotional support[21] … models with pretrained embeddings ELMo[17] and a recurrent convolutional neural network model [13 … and EVI to answer general facts and background questions about our chatbot …
Full-attention Based Drug Drug Interaction Extraction Exploiting User-generated Content
B Xu, X Shi, Z Zha, W Zheng, H Lin… – 2018 IEEE …, 2018 – ieeexplore.ieee.org
… of papers showed that it has performed well in several NLP tasks such as machine translation [7] and dialogue system [8]. Inspired … Y. Liu, Y. Chen, WX Zhao, D. Yu, and H. Wu, “Multi-turn response selection for chatbots with deep … extraction via convolutional neural networks,” vol …
State-of-the-Art Approaches for German Language Chat-Bot Development
N Boisgard – 2018 – ec.tuwien.ac.at
… Figure 1.2: The number of publications containing the terms “chatbot”, “chatterbot”, “chat-bot” or “chat bot … 2.1: A list of the terms proposed as synonymous to the term “chat-bot” in the … as in lieu with the classification used in [Jurafsky and Martin, 2017a], chat-bots being merely chit …
Natural Language Generation with Neural Variational Models
H Bahuleyan – arXiv preprint arXiv:1808.09012, 2018 – arxiv.org
… 19 2.2.9 Dialog Systems … AI Artificial Intelligence 1, 2 ANN Artificial Neural Networks 8, 10 CNN Convolutional Neural Network 8 … Consider a conversational system such as a chatbot, where x is the line input by the user and y is the line generated by the machine …
Orange Technologies (ICOT)
M Dong, L Wang, Y Lu, H Li – ieeexplore.ieee.org
… 44 Stem Cell Detection based on Convolutional Neural Network via Third … Exploring Microscopic Fluctuation of Facial Expression for Mood Disorder Classification Ming-Hsiang Su, Chung-Hsien Wu, Kun-Yi Huang, Qian-Bei Hong and Hsin-Min Wang #09 A Chatbot Using LSTM …
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
J Han, Z Zhang, N Cummins, B Schuller – arXiv preprint arXiv:1809.08927, 2018 – arxiv.org
… type-based: in addition, several GAN variants have been named after the network topology used in the GAN configuration, such as the DCGAN based on deep convolutional neural networks [19], the … an ultimate goal to automatically adjust the sentiment of a chatbot response [93 …
Design of an Intelligent Cognition Assistant for People with Cognitive Impairment
YT Tsai, WA Lin – 2018 IEEE 20th International Conference on …, 2018 – ieeexplore.ieee.org
… Applications of chat robots (ChatBots or conversational interfaces) have received much attention … 3. The components of a ChatBot, adopted from [33] C. Conversation Management … Charif, “Activity recognition for indoor fall detection using convolutional neural network,” in 2017 …
Quantifying Chatbot Performance by using Data Analytics
CM Jongerius – 2018 – dspace.library.uu.nl
… 7 (Venturebeat, 2016). However, as mentioned in the introduction, businesses implementing chatbots should recognize the likelihood that the chatbot’s performance is divergent from that of a human. For this reason, businesses …
Data-Driven Input Feature Augmentation for Named Entity Recognition
??? – 2018 – s-space.snu.ac.kr
… language understanding (SLU) component of dialog management systems, or ”chatbots”, along with the intent detection classification task. In the common … For example, in an automatic restaurant search query system, the dialog system may have …
Natural language generation for commercial applications
A van de Griend, W OOSTERHEERT, T HESKES – 2018 – ru.nl
… in terms of NLG, not much has changed since the first chatbot, Eliza (Weizenbaum … it may be used to improve responses from retrieval-based chatbots (section 8.3) … architecture: a recurrent neural network (RNN) (section 3.1) and a Convolutional Neural Network (CNN) (section …
Fact or Fiction
A Lu, CJ Lovering, N Dinh, C Tri, T Nguyen, H Bui – 2018 – digitalcommons.wpi.edu
… 1 Additionally, Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNNs), two popular general architectures, both … to get sentiment scores on entities, provide a framework to build chatbots upon, and … with users, such as a dialog system or a chatbot, can pass …
Engagement Recognition based on Multimodal Behaviors for Human-Robot Dialogue
K Inoue – 2018 – repository.kulib.kyoto-u.ac.jp
… Chapter 1 describes the background of this thesis in the context of spoken dialogue systems and human-robot interaction in order to clarify problems and approaches. Chapter 2 … engagement recognition for live spoken dialogue systems. Backchannels and laughing …
Deep Reinforcement Learning
CC Aggarwal – Neural Networks and Deep Learning, 2018 – Springer
… For example, a straw-man (but not very good) algorithm for chess might use the same ?-greedy algorithm of Section 9.3.2, but the values of actions are computed by using the board state as input to a convolutional neural network …
Can Computers Create Art?
A Hertzmann – Arts, 2018 – mdpi.com
… Networks (Elgammal et al. 2017); it is a visual style that seems familiar but not the same as what we are familiar with (and is probably driven, in part, by the biases of the convolutional neural network representation). In each of these …
Multimodal Differential Network for Visual Question Generation
BN Patro, S Kumar, VK Kurmi… – arXiv preprint arXiv …, 2018 – arxiv.org
… Current dialog systems as evaluated in (Chattopadhyay et al., 2017) show that when trained between bots, AI-AI dialog sys- tems show improvement, but that … Generating a natural and engaging question is an interesting and challenging task for a smart robot (like chat-bot) …
CSIndexbr: Exploring the Brazilian Scientific Production in Computer Science
MT Valente, K Paixão, D Hafner, D Tran, A Irpan… – arXiv preprint arXiv …, 2018 – arxiv.org
PacGAN: The power of two samples in generative adversarial networks
AK Khetan – 2018 – ideals.illinois.edu
… tion [10]), as well as dialogue systems or chatbots—applications where one may need realistic but artificially generated data … Similarly, when packing a DCGAN, which uses convolutional neural networks for both the …
Hands-On Natural Language Processing with Python: A practical guide to applying deep learning architectures to your NLP applications
R Arumugam, R Shanmugamani – 2018 – books.google.com
… Chatbot’s are becoming an integrated part of any website; while virtual assistants are … 21 Coreference resolution 21 Searching 22 Question answering and chatbots 23 Converting … 61 Batch normalization 61 L1 and L2 normalization 61 Convolutional Neural Network 62 Kernel …
Finding Good Representations of Emotions for Text Classification
JH Park – arXiv preprint arXiv:1808.07235, 2018 – arxiv.org
… from a convolutional neural network model with an emotion-labeled corpus, which is … When training NLP models, such as chatbots, things do not always go as intended. Famous incident of Microsoft chatbot Tay, which learned directly from users’ tweets with …
In-Vehicle Voice Interface with Improved Utterance Classification Accuracy Using Off-the-Shelf Cloud Speech Recognizer
T Homma, Y Obuchi, K Shima, R Ikeshita… – … on Information and …, 2018 – search.ieice.org
… The class labels are usually annotated by humans. Once we prepare the training data, we can train the utterance classifier by using a machine learning algorithm such as naive Bayes [26], logistic regres- sion [9], or convolutional neural network [28] …
Natural Language Processing and Chinese Computing: 7th CCF International Conference, NLPCC 2018, Hohhot, China, August 26–30, 2018, Proceedings
M Zhang, V Ng, D Zhao, S Li, H Zan – 2018 – books.google.com
Page 1. Min Zhang· Vincent Ng· Dongyan Zhao Sujian Li· Hongying Zan (Eds.) Natural Language Processing and Chinese Computing 7th CCF International Conference, NLPCC 2018 Hohhot, China, August 26–30, 2018 Proceedings, Part I 123 Page 2 …
Artificial Intelligence for All: An Abiding Destination
V Pathak, P Tiwari – 2018 – books.google.com
… of Learning Python Unsupervised Learning Introducing Unsupervised Learning Rule Association Cluster Analysis Anomaly Detection Dimensionality Reduction CHAPTER VI Convolutional and Recurrent Neural Networks Convolutional Neural Networks Recurrent Neural …
Selecting and Generating Computational Meaning Representations for Short Texts
C Finegan-Dollak – 2018 – deepblue.lib.umich.edu
Page 1. Selecting and Generating Computational Meaning Representations for Short Texts by Catherine Finegan-Dollak A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy …
Using Polarity Classification Model to Assess Customer Attitudes: the Case of Russian E-Commerce Companies on Twitter
T Alexander – 2018 – dspace.spbu.ru
… deals with human-computer language interaction (Devika et al., 2016). It is applied in such spheres, as spam filtering, search recommendations and chat bots. One of NLP subdomains — Sentiment Analysis (SA) — is specifically …
Semantic decomposition and marker passing in an artificial representation of meaning
DG Design – Computer Communications, 2018 – masp.dai-labor.de
… Residential Short-Term Load Forecasting Using Convolutional Neural Networks Marcus Voß, Christian Bender-Saebelkampf, and Sahin Albayrak In: 2018 … A Next Generation Chatbot- Framework for the Public Administration Andreas Lommatzsch In: Proceedings of the 18th …
Artificial Intelligence And Machine Learning And Marketing Management
J Seligman – 2018 – books.google.com
… text into a text box and obtaining results or being able to discuss issues with other people, popularly known as a chatbot … Chatbots ? Personalized web pages and apps ? Finalizing leads ? Optimizing Ad targeting ? Predictive analysis What Can Marketing Expect in the Future …