Notes:
Neural Turing machines, or NTMs, are a type of artificial neural network that is designed to have the fuzzy pattern matching capabilities of traditional neural networks, as well as the ability to use algorithms and other types of programs to perform computational tasks. This allows NTMs to have a more flexible and powerful computational ability than traditional neural networks, and allows them to be used for a wider range of tasks. NTMs were first proposed by researchers in 2014 and have since been the subject of ongoing research and development in the fields of artificial intelligence and machine learning.
In the context of dialog systems, NTMs could potentially be used to enable the system to store and manipulate information in a more flexible and efficient way. For example, an NTM could be used to store and retrieve information about a user’s preferences, history, and context, and to use this information to generate more personalized and relevant responses to the user’s queries and requests. NTMs could also potentially be used to enable the system to learn and adapt to new information and situations in a more flexible and sophisticated way.
Resources:
- github.com/npow/MemN2N .. end-to-end memory networks in theano
Wikipedia:
References:
See also:
100 Best DeepMind Videos | Neural Network & Dialog Systems 2018 | Neural Network Meta Guide
An end-to-end goal-oriented dialog system with a generative natural language response generation
S Constantin, J Niehues, A Waibel – arXiv preprint arXiv:1803.02279, 2018 – arxiv.org
… Memory Networks: the Recurrent Entity Networks [5], the Differentiable Neural Computer [4], the Neural Turing Machine [3], and … A Dialog System with a Generative NLG 11 … URL http://arxiv.org/ abs/1511.06931v6 [3] Graves, A., Wayne, G., Danihelka, I.: Neural turing machines …
Dynamic neural turing machine with continuous and discrete addressing schemes
C Gulcehre, S Chandar, K Cho, Y Bengio – Neural computation, 2018 – MIT Press
… Next section. We extend the neural Turing machine (NTM) model into a dynamic neural Turing machine (D-NTM) by introducing trainable address vectors … We provide extensive analysis of our model and compare different variations of neural Turing machines on this task …
Extending neural generative conversational model using external knowledge sources
P Parthasarathi, J Pineau – arXiv preprint arXiv:1809.05524, 2018 – arxiv.org
… out and mechanisms were introduced in models like Memory Networks (Sukhbaatar et al., 2015; Bordes et al., 2016; Gulcehre et al., 2018), and the Neural Turing Machine (Graves et … Neural turing machines … In the 6th Dialog System Technology Challenges (DSTC6) Workshop …
Few-Shot Generalization Across Dialogue Tasks
V Vlasov, A Drissner-Schmid, A Nichol – arXiv preprint arXiv:1811.11707, 2018 – arxiv.org
… Our attention architecture consists of a modified version of a Neural Turing Machine [24] … Example-based dialog modeling for practical multi-domain dialog system. Speech Communication, 51(5):466–484, 2009 … Neural turing machines. arXiv preprint arXiv:1410.5401, 2014 …
Multimodal architecture for video captioning with memory networks and an attention mechanism
W Li, D Guo, X Fang – Pattern Recognition Letters, 2018 – Elsevier
… Recently, memory networks [8], [9], [10], with the potential to capture long-term correlations in sequential problems, achieve great success in question answering [11] and dialog systems [12]. Among such investigations, Neural Turing Machine [8] shows a great advantage of …
Hierarchical variational memory network for dialogue generation
H Chen, Z Ren, J Tang, YE Zhao, D Yin – … of the 2018 World Wide Web …, 2018 – dl.acm.org
… 33, 34, 38, 41] have been proved to be capable in multiple dialogue system applications with … 6]: (1) Meaningless responses: Given a wide range of contexts, dialogue systems trained via … Inspired by the writ- ing mechanism of neural turing machines [14], we utilize a Forget and …
Multiresolution recurrent neural networks: An application to dialogue response generation
IV Serban, T Klinger, G Tesauro… – Thirty-First AAAI …, 2017 – aaai.org
… The coarse prediction encoder GRU RNN has 500 hidden units. Evaluation Methods It has long been known that accurate evaluation of dialogue system responses is difficult (Schatzmann, Georgila, and Young 2005). Liu et al …
Neural networks for information retrieval
T Kenter, A Borisov, C Van Gysel, M Dehghani… – Proceedings of the 40th …, 2017 – dl.acm.org
… Examples are conversational and dialog systems [7, 34, 54] or ma- chine reading and question answering tasks where the … 15 mins Conversational IR/dialogue systems – Unlike QA systems, conversational systems should maintain a state of a session … Neural turing machines …
Knowledge-aware multimodal dialogue systems
L Liao, Y Ma, X He, R Hong, T Chua – 2018 ACM Multimedia Conference …, 2018 – dl.acm.org
… 4.1 Experimental Setups 4.1.1 Datasets. Arguably the greatest bottleneck for statistical approaches to dialogue system development is the collection of appropriate training dataset, and this is especially true for task- oriented dialogue systems [40] …
Memory-augmented Dialogue Management for Task-oriented Dialogue Systems
Z Zhang, M Huang, Z Zhao, F Ji, H Chen… – arXiv preprint arXiv …, 2018 – arxiv.org
… In order to decide the next action a dialogue system should take, dialogue management … Memory-augmented Dialogue Management for Task-oriented Dialogue Systems … Neural turing machines [9] was proposed to augment existing neural models with additional memory units …
Investigating deep reinforcement learning techniques in personalized dialogue generation
M Yang, Q Qu, K Lei, J Zhu, Z Zhao, X Chen… – Proceedings of the 2018 …, 2018 – SIAM
… support services to entertaining chatbots. Generally, there are two different kinds of dialogue systems: the goal-oriented dialogue system and the non-goal-oriented (open domain) dialogue system. In this paper, we focus on …
Data distillation for controlling specificity in dialogue generation
J Li, W Monroe, D Jurafsky – arXiv preprint arXiv:1702.06703, 2017 – arxiv.org
… Generic responses in open-domain dialogue End-to-end dialogue systems (Ritter et al., 2011; Serban et al., 2016c; Vinyals and Le, 2015; Ser- ban et al., 2016d,a; Asghar et al … A good dialogue system should have the ability to decide when to say generic things and when not to …
Global-to-local Memory Pointer Networks for Task-Oriented Dialogue
CS Wu, R Socher, C Xiong – arXiv preprint arXiv:1901.04713, 2019 – arxiv.org
… 1 INTRODUCTION Task-oriented dialogue systems aim to achieve specific user goals such as restaurant reservation or navigation inquiry within a limited dialogue turns via natural language … 4 RELATED WORKS Task-oriented dialogue systems …
Text Representation using Convolutional Networks
X Zhang – 2019 – search.proquest.com
… systems. Dr. Antoine Bordes and Dr. Jason Weston from Facebook AI Research … space. One example is a free-style dialogue system in which reasoning from some … the neural Turing machine of which a controller network can be trained to utilize an external memory matrix …
Eigen: A step towards conversational AI
WH Guss, J Bartlett, P Kuznetsov… – Alexa Prize …, 2017 – m.media-amazon.com
… Dialogue systems already form the basis behind many customer support hotlines, chatbot user interfaces, and digital personal assistants like Amazon … A stronger approach is to augment the recurrent model with a memory matrix, inspired by neural turing machines Graves et al …
Exploring Implicit Feedback for Open Domain Conversation Generation
WN Zhang, L Li, D Cao, T Liu – Thirty-Second AAAI Conference on Artificial …, 2018 – aaai.org
… Learning for Conversation Model The reinforcement learning approach has been widely used in conversation or dialogue systems (Walker 2000 … 2016 presented an end-to-end dialogue system for information accquisition from knowledge base by using reinforcement learning …
CoChat: Enabling bot and human collaboration for task completion
X Luo, Z Lin, Y Wang, Z Nie – Thirty-Second AAAI Conference on Artificial …, 2018 – aaai.org
… Wen et al. (2016) propose a neural-network-based trainable dialog system along with a new way of collecting dialog data. Bor- des and Weston (2016) report an end-to-end dialog system based on Memory Networks that can achieve promising, yet imperfect, performance …
Knowledge acquisition for visual question answering via iterative querying
Y Zhu, JJ Lim, L Fei-Fei – Proceedings of the IEEE …, 2017 – openaccess.thecvf.com
… Knowledge acquisi- tion has been a major interest of AI research for decades. One remarkable pioneer work, dating back to the 1970s, is SHRDLU [36], which provided a dialog system for users to query a computer about the state of a simplified blocks world …
Recurrent Neural Network with Dynamic Memory
J Bai, T Dong, X Liao, N Mu – International Symposium on Neural …, 2018 – Springer
… In [16], the authors have proposed the neural turing machine (NTM) … 23(3), 530–539 (2015)CrossRefGoogle Scholar. 2. Korpusik, M., Glass, J.: Spoken language understanding for a nutrition dialogue system … Graves, A., Wayne, G., Danihelka, I.: Neural turing machines …
Mem2seq: Effectively incorporating knowledge bases into end-to-end task-oriented dialog systems
A Madotto, CS Wu, P Fung – arXiv preprint arXiv:1804.08217, 2018 – arxiv.org
Page 1. Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems … Abstract End-to-end task-oriented dialog systems usually suffer from the challenge of in- corporating knowledge bases …
Dynamic Memory Networks for Dialogue Topic Tracking
S Kim – 2019 – workshop.colips.org
… However, the current dialogue systems still have limited capabilities of conducting a coherent conversation across multiple different topics, which is … While many previous studies on multi-topic dialogue processing aimed at directly building dialogue system components for topic …
Progressive Memory Banks for Incremental Domain Adaptation
N Asghar, L Mou, KA Selby, KD Pantasdo… – arXiv preprint arXiv …, 2018 – arxiv.org
… Colors indicate different domains. neural) Turing machine. Zhang et al. (2018) combine the above two styles of memory for task-oriented dialog systems, where they have both slot-value memory and read- and-write memory …
Towards conversational search and recommendation: System ask, user respond
Y Zhang, X Chen, Q Ai, L Yang, WB Croft – Proceedings of the 27th ACM …, 2018 – dl.acm.org
… Conversational search is closely related to several other research topics such as dialog systems, traditional web search, and faceted search. Recent conversational search systems are well integrated with state-of-the-art dialog system models, and by focusing on the search task …
Integrating both visual and audio cues for enhanced video caption
W Hao, Z Zhang, H Guan – Thirty-Second AAAI Conference on Artificial …, 2018 – aaai.org
… Neural Turing Machine(NTM) (Graves, Wayne, and Dani- helka 2014), memory network (Weston, Chopra, and Bordes 2014), which is simply … needs long temporal depen- dency, such as visual question answering (Xiong, Merity, and Socher 2016) and dialog systems (Dodge et …
Memory augmented neural networks with wormhole connections
C Gulcehre, S Chandar, Y Bengio – arXiv preprint arXiv:1701.08718, 2017 – arxiv.org
… Memory augmented neural networks (MANN) such as neural Turing machines (NTM) (Graves et al., 2014; Rae et al., 2016), dynamic NTM (D-NTM … The neural Turing machine (NTM) (Graves et al., 2014) is such an example of a MANN, with both reading and writing into the …
Learning to activate logic rules for textual reasoning
Y Yao, J Xu, J Shi, B Xu – Neural Networks, 2018 – Elsevier
… More complex methods include Neural Turing Machines (Graves, Wayne, & Danihelka, 2014), Dynamic Neural Turing Machines (Gulcehre, Chandar, Cho, & Bengio, 2016), Neural Random-Access Machines (Kurach, Andrychowicz, & Sutskever, 2016) and Differentiable Neural …
Deep Bayesian learning and understanding
JT Chien – Proceedings of the 27th International Conference on …, 2018 – aclweb.org
… GAN (Yu et al., 2017) and reinforcement learning (Tegho et al., 2017) are introduced in various deep models which open a window to more practical tasks, eg reading comprehension, sentence generation, dialogue system, question answering … 2014. Neural Turing machines …
External Memory Enhanced Sequence-to-Sequence Dialogue Systems
J Verdegaal – 2018 – pdfs.semanticscholar.org
… Sequence-to-Sequence Dialogue Systems … The goal of this work is not to develop a dialogue system to compete in the Turing- test, but to investigate techniques which can be added to state-of-the-art end-to-end models in a straightforward manner to improve content of replies of …
Visual reasoning with multi-hop feature modulation
F Strub, M Seurin, E Perez, H De Vries… – Proceedings of the …, 2018 – openaccess.thecvf.com
Page 1. Visual Reasoning with Multi-hop Feature Modulation Florian Strub1, Mathieu Seurin1, Ethan Perez2,3, Harm de Vries2, Jérémie Mary 4, Philippe Preux 1, Aaron Courville2,5 Olivier Pietquin6 1Univ. Lille, CNRS, Inria …
Learning Generative End-to-end Dialog Systems with Knowledge
T Zhao – 2017 – cs.cmu.edu
… 2.1 Dialog System Pipeline for Task-oriented Dialog Systems … Figure 2.1: Dialog System Pipeline for Task-oriented Dialog Systems are created to alleviate this problem by automatically learning intermediate features in high-dimension distributed representations. Wen, et al …
Conversational memory network for emotion recognition in dyadic dialogue videos
D Hazarika, S Poria, A Zadeh, E Cambria… – Proceedings of the …, 2018 – aclweb.org
Page 1. Proceedings of NAACL-HLT 2018, pages 2122–2132 New Orleans, Louisiana, June 1 – 6, 2018. c 2018 Association for Computational Linguistics Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos …
Cross-language neural dialog state tracker for large ontologies using hierarchical attention
Y Jang, J Ham, BJ Lee, KE Kim – IEEE/ACM Transactions on …, 2018 – ieeexplore.ieee.org
… and 5: The neural network has widely used in natural language processing (NLP) tasks, such as sentence clas- sification, machine translation, and dialog systems [11]–[15] … Another approach uses external memory, such as a neural turing machine [21] and mem- ory network [22 …
Interactive language acquisition with one-shot visual concept learning through a conversational game
H Zhang, H Yu, W Xu – arXiv preprint arXiv:1805.00462, 2018 – arxiv.org
Page 1. Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game Haichao Zhang†, Haonan Yu†, and Wei Xu †§ † Baidu Research – Institue of Deep Learning, Sunnyvale USA …
Sequence-to-sequence prediction of personal computer software by recurrent neural network
Q Yang, Z He, F Ge, Y Zhang – 2017 International Joint …, 2017 – ieeexplore.ieee.org
… 2342-2350, 2015. [16] A. Graves, G. Wayne, I. Danihelka, “Neural turing machines,” [Online], Available: Arxiv Preprint arXiv … [23] VS Iulian, S. Alessandro, Y. Bengio, A. Courville, J. Pineau, “Building End-to-End Dialogue Systems Using Generative Hierarchical Neural Network …
Relational dynamic memory networks
T Pham, T Tran, S Venkatesh – arXiv preprint arXiv:1808.04247, 2018 – arxiv.org
… Notable architectures include Neural Turing Machine [23] and its recent cousin, the Differentiable Neural Computer [24 … of applications, including question-answering [37, 62], graph processing [24], algorithmic tasks [24], meta-learning [53], healthcare [40] and dialog systems [39 …
Coupling distributed and symbolic execution for natural language queries
L Mou, Z Lu, H Li, Z Jin – … of the 34th International Conference on …, 2017 – dl.acm.org
Page 1. Coupling Distributed and Symbolic Execution for Natural Language Queries Lili Mou 1 Zhengdong Lu 2 Hang Li 3 Zhi Jin 1 Abstract Building neural networks to query a knowledge base (a table) with natural language is an emerg- ing research topic in deep learning …
Rich short text conversation using semantic-key-controlled sequence generation
K Yu, Z Zhao, X Wu, H Lin, X Liu – IEEE/ACM Transactions on Audio …, 2018 – dl.acm.org
… A memory reader R conducts reading process on the selected memory block m to produce an external memory context vector r r = R(m, p). (10) Inspired by Neural Turing Machine [26], we apply a content- based addressing to the matrix of the chosen memory cell …
Deep Learning and Hierarchical Reinforcement Learning for modeling a Conversational Recommender System
P Basile, C Greco, A Suglia… – Intelligenza …, 2018 – content.iospress.com
… A CRS can be considered a goal-driven dialogue system whose main goal, due to its complexity, can be solved effectively by dividing it … Deep Learning (DL) architectures are widely used in dialogue systems [30, 32, 38] and are able to achieve good performance in generating …
ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection
D Hazarika, S Poria, R Mihalcea, E Cambria… – Proceedings of the …, 2018 – aclweb.org
… This can help in creating empathetic dialogue systems, thus improving the overall human-computer interaction experience (Young et al., 2018). Analyzing emotional dynamics in conversations, however, poses complex challenges …
Graph Enhanced Memory Networks for Sentiment Analysis
Z Xu, R Vial, K Kersting – Joint European Conference on Machine Learning …, 2017 – Springer
… information. Typical examples include tree structure of a sentence and knowledge graph in a dialogue system. In … 12. Graves, A., Wayne, G., Danihelka, I.: Neural turing machines. arXiv preprint: arXiv:1410.5401 (2014). 13. Graves …
An attention-based long-short-term-memory model for paraphrase generation
K Nguyen-Ngoc, AC Le, VH Nguyen – International Symposium on …, 2018 – Springer
… learning [31] have been used to solve various tasks in NLP with effective results: language modeling [32], machine translation [4], speech recognition [15], and dialogue systems [26] … IEEE (2013)Google Scholar. 7. Graves, A., Wayne, G., Danihelka, I.: Neural turing machines …
Learning to remember translation history with a continuous cache
Z Tu, Y Liu, S Shi, T Zhang – Transactions of the Association for …, 2018 – MIT Press
Page 1. Learning to Remember Translation History with a Continuous Cache Zhaopeng Tu Tencent AI Lab zptu@tencent.com Yang Liu Tsinghua University liuyang2011@tsinghua. edu.cn Shuming Shi Tencent AI Lab shumingshi@tencent.com …
Feature-based compositing memory networks for aspect-based sentiment classification in social internet of things
R Ma, K Wang, T Qiu, AK Sangaiah, D Lin… – Future Generation …, 2019 – Elsevier
… Recent researchers focus on extending deep neural networks with external memory, such as the Neural Turing Machine which uses a continuous memory representation with both content and address-based access [26]. Weston et al …
A Hierarchical Conditional Attention-Based Neural Networks for Paraphrase Generation
K Nguyen-Ngoc, AC Le, VH Nguyen – International Conference on Multi …, 2018 – Springer
… 33] have been effectively used to solve various tasks in NLP as below: language modeling [34], machine translation [4], speech recognition [15], and dialogue systems [30] … arXiv preprint arXiv:1302.4389 (2013). 7. Graves, A., Wayne, G., Danihelka, I.: Neural turing machines …
Review of state-of-the-art in deep learning artificial intelligence
VV Shakirov, KP Solovyeva… – Optical Memory and …, 2018 – Springer
… 9. PROSPECTIVE DIRECTIONS IN NEUROMORHIC AI TECHNOLOGIES Recently, a flow of articles about memory networks and neural Turing machines made it possible to use arbitrarily large memories while preserving reasonable number of model parameters …
Few-shot classification in named entity recognition task
A Fritzler, V Logacheva, M Kretov – … of the 34th ACM/SIGAPP Symposium …, 2019 – dl.acm.org
… used in various information extraction frame- works and is one of the core components of goal-oriented dialogue systems [23] … In [16] the authors demonstrate that memory-augmented neural networks, such as Neural Turing Machines, have a capacity to perform meta- learning …
Attention and Memory Augmented Networks
U Kamath, J Liu, J Whitaker – Deep Learning for NLP and Speech …, 2019 – Springer
… have used attention mechanisms for tasks such as sentence embedding, language modeling, machine translation, syntactic constituency parsing, document classification, sentiment classification, summarization, and dialog systems to name a few … 9.3.3 Neural Turing Machines …
Improving End-to-End Memory Networks with Unified Weight Tying
F Liu, T Cohn, T Baldwin – Proceedings of the Australasian Language …, 2017 – aclweb.org
… In this work, we employ a collection of goal-oriented dialog tasks, Dialog bAbI, all in a restaurant reservation scenario, developed by Bordes and Weston (2016), consisting of 6 cate- gories each with a specific focus on tasking on aspect of an end-to-end dialog system: 1. issu …
Improved dynamic memory network for dialogue act classification with adversarial training
Y Wan, W Yan, J Gao, Z Zhao, J Wu… – … Conference on Big …, 2018 – ieeexplore.ieee.org
… widely adopted in computational linguists, especially in the dialogue system. The automatic recognition of DAs is an important step toward understanding spontaneous dialogue, which will facilitate many applications such as human-computer dialogue systems [1], language …
Rotational Unit of Memory: A Novel Representation Unit for RNNs with Scalable Applications
R Dangovski, L Jing, P Nakov, M Tatalovi?… – Transactions of the …, 2019 – MIT Press
… from language modeling, part-of-speech tagging and named entity recognition to neural machine translation, text summarization, question answering, and building chatbots/ dialog systems … Another alternative is the use of neural Turing machines (Graves et al., 2014, 2016) …
Object-oriented neural programming (oonp) for document understanding
Z Lu, X Liu, H Cui, Y Yan, D Zheng – arXiv preprint arXiv:1709.08853, 2017 – arxiv.org
… (2009) on modeling parsing as a decision process, and the work on state-tracking models in dialogue system (Henderson et … Similar to the controller in Neural Turing Machine (Graves et al., 2014), Neural Net Controller is equipped with multi- ple read-heads and write-heads for …
Hybrid Deep Open-Domain Question Answering
A Aghaebrahimian – Proceedings of the 8th Language and …, 2017 – ufal.mff.cuni.cz
… Cloze-type QA provides a dependable measure for MC. However, in QA systems for other pur- poses like in dialogue systems or scientist’s as- sistants, the answers are not known in advance. It is assumed that they are available somewhere on the Internet …
Reinforced video captioning with entailment rewards
R Pasunuru, M Bansal – arXiv preprint arXiv:1708.02300, 2017 – arxiv.org
Page 1. arXiv:1708.02300v1 [cs.CL] 7 Aug 2017 Reinforced Video Captioning with Entailment Rewards Ramakanth Pasunuru and Mohit Bansal UNC Chapel Hill {ram, mbansal}@cs.unc.edu Abstract Sequence-to-sequence …
Deep reinforcement learning: An overview
Y Li – arXiv preprint arXiv:1701.07274, 2017 – arxiv.org
… Then we discuss various appli- cations of RL, including games, in particular, AlphaGo, robotics, natural language processing, including dialogue systems, machine translation, and text generation, computer vision, business management, finance, healthcare, education, Industry …
Amanuensis: The Programmer’s Apprentice
T Dean, M Chiang, M Gomez, N Gruver, Y Hindy… – arXiv preprint arXiv …, 2018 – arxiv.org
Page 1. Amanuensis: The Programmer’s Apprentice Thomas Dean1,2 Maurice Chiang2 Marcus Gomez2 Nate Gruver2 Yousef Hindy2 Michelle Lam2 Peter Lu2 Sophia Sanchez2 Rohun Saxena2 Michael Smith2 Lucy Wang2 Catherine Wong2 Abstract …
On the Duration, Addressability, and Capacity of Memory-Augmented Recurrent Neural Networks
Z Quan, Z Gao, W Zeng, X Li, M Zhu – IEEE Access, 2018 – ieeexplore.ieee.org
… Meanwhile, the duration, addressability, and capacity are applied to analyze and compare two M-RNNs: long short term memory (LSTM) and neural Turing machine (NTM) for different cases … [9], neural Turing machines (NTMs) [10], dynamic memory networks (DMN) [11], fast …
An Attention-Based Long-Short-Term-Memory Model for Paraphrase Generation
NK Nguyen, AC Le, VH Nguyen – 2018 – researchgate.net
… Recently, the prevalent approach to sequence to sequence (seq2seq) learning [31] have been used to solve various tasks in NLP with effective results: language modeling [32], machine translation [4], speech recognition [15], and dialogue systems [26] … Neural turing machines …
Direct optimization of F-measure for retrieval-based personal question answering
R Fakoor, A Kainth, S Shakeri… – 2018 IEEE Spoken …, 2018 – ieeexplore.ieee.org
… Recent advances in speech recognition [1, 2], speech en- hancement [3, 4], natural language understanding [5, 6], question answering [7, 8, 9], and dialogue systems [10, 11] have fueled the current surge in research and development for smart personal assistants [12] like Alexa …
Joint Learning of Question Answering and Question Generation
Y Sun, D Tang, N Duan, S Liu, Z Yan… – … on Knowledge and …, 2019 – ieeexplore.ieee.org
Page 1. 1041-4347 (c) 2018 IEEE. Personal use is permitted, but republication/ redistribution requires IEEE permission. See http://www.ieee.org/ publications_standards/publications/rights/index.html for more information. This …
Deep learning for sentiment analysis: A survey
L Zhang, S Wang, B Liu – Wiley Interdisciplinary Reviews: Data …, 2018 – Wiley Online Library
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Learning to Memorize in Neural Task-Oriented Dialogue Systems
CS Wu – arXiv preprint arXiv:1905.07687, 2019 – arxiv.org
Page 1. Learning to Memorize in Neural Task-Oriented Dialogue Systems by Chien-Sheng (Jason) Wu A Thesis Submitted to … Chien-Sheng (Jason) Wu June 2019 ii Page 3. Learning to Memorize in Neural Task-Oriented Dialogue Systems by Chien-Sheng (Jason) Wu …
Proactive Communication in Human-Agent Teaming
EM van Zoelen – 2019 – dspace.library.uu.nl
… Because of this, the resulting dialogue system can only be used for very narrow … Young et al., 2013), these problems must be solved before dialogue systems using POMDPs … Neural Networks (Memory Networks/Neural Turing Machines) More recently, people have tried to create …
Coevolutionary recommendation model: Mutual learning between ratings and reviews
Y Lu, R Dong, B Smyth – Proceedings of the 2018 World Wide Web …, 2018 – dl.acm.org
… Most widely used neural network structures, including convolu- tional neural networks [17, 20], recurrent neural networks [27, 37], and neural turing machine [44], have shown promising results in various natural language processing benchmarks …
Source Separation and Machine Learning
JT Chien – 2018 – books.google.com
… 299 7.5.3 System Evaluation . . . . . 304 Neural Turing Machine . . . . . 307 7.6.1 Memory Augmented Source Separation …
Converse-et-impera: Exploiting deep learning and hierarchical reinforcement learning for conversational recommender systems
C Greco, A Suglia, P Basile, G Semeraro – Conference of the Italian …, 2017 – Springer
… performed by a speaker or by a hearer. A CRS can be considered a goal-driven dialogue system whose main goal, due to its complexity, can be solved effectively by dividing it in simpler goals. Indeed, a dialogue with this kind of …
Deep Learning For Nlp And Speech Recognition
ULIU KAMATH, J WHITAKER – 2019 – Springer
Page 1. Uday Kamath · John Liu · James Whitaker Deep Learning for NLP and Speech Recognition Page 2. Deep Learning for NLP and Speech Recognition Page 3. Uday Kamath • John Liu • James Whitaker Deep Learning for NLP and Speech Recognition 123 Page 4 …
Recurrent neural networks for structured data
TTM Pham – 2019 – dro.deakin.edu.au
… 54 3.5.2 Neural Turing Machine … MemNN Memory Network MXM Multi-X Modular NCE Noise-Contrastive Estimation NTM Neural Turing Machine QA Question Answering ReLU Rectified Linear Unit RF Random Forest RNCC Recurrent Neural Collective Classification …
Recurrent Neural Networks
U Kamath, J Liu, J Whitaker – Deep Learning for NLP and Speech …, 2019 – Springer
In the previous chapter, CNNs provided a way for neural networks to learn a hierarchy of weights, resembling that of n-gram classification on the text. This approach proved to be very effective for…
A hybrid approach with optimization-based and metric-based meta-learner for few-shot learning
D Wang, Y Cheng, M Yu, X Guo, T Zhang – Neurocomputing, 2019 – Elsevier
… For metric-based works, [15] exploits Neural Turing Machine (NTM) [16], a famous memory-augmented neural network, to few-shot learning problem and introduces a new attention-based memory accessing method to rapidly assimilate new data used for accurate …
Tackling Sequence to Sequence Mapping Problems with Neural Networks
L Yu – arXiv preprint arXiv:1810.10802, 2018 – arxiv.org
Page 1. Tackling Sequence to Sequence Mapping Problems with Neural Networks Lei Yu Mansfield College University of Oxford A thesis submitted for the degree of Doctor of Philosophy Trinity 2017 arXiv:1810.10802v1 [cs.CL] 25 Oct 2018 Page 2 …
Deep Memory Networks for Natural Conversations
2017 – s-space.snu.ac.kr
Cognitive computing: Where big data is driving us
AP Appel, H Candello, FL Gandour – Handbook of Big Data Technologies, 2017 – Springer
In this chapter we will discuss the concepts and challenges to design Cognitive Systems. Cognitive Computing is the use of computational learning systems to augment cognitive capabilities in solving…
Nonrecurrent neural structure for long-term dependence
S Zhang, C Liu, H Jiang, S Wei, L Dai, Y Hu… – IEEE/ACM Transactions …, 2017 – dl.acm.org
… For example, the so-called neural turing machines (NTM) [29] are proposed to improve the memory of neural networks by coupling with external memory resources, which can learn to sort a small set of numbers as well as other symbolic manipulation tasks …
Recurrent Neural Networks
CC Aggarwal – Neural Networks and Deep Learning, 2018 – Springer
… The neural Turing machine is discussed in Chapter 10, which uses external memory to improve the stability of neural network learning. A neural Turing machine can be shown to be equivalent to a recurrent neural network, and …
Efficient Deep Reinforcement Learning via Planning, Generalization, and Improved Exploration
J Oh – 2018 – deepblue.lib.umich.edu
Page 1. Efficient Deep Reinforcement Learning via Planning, Generalization, and Improved Exploration by Junhyuk Oh A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Computer Science and Engineering) …
Incremental generative models for syntactic and semantic natural language processing
JM Buys – 2017 – ora.ox.ac.uk
… cations. Examples include automatic speech recognition, machine translation, text pre- diction on mobile keyboards, automatic e-mail reply suggestions and dialogue systems. Further language generation tasks that recently drew attention include image caption gen …
End-to-end information extraction from business documents
RB Palm – 2019 – orbit.dtu.dk
Page 1. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise …
Deep reinforcement learning for sequence to sequence models
Y Keneshloo, T Shi, N Ramakrishnan… – arXiv preprint arXiv …, 2018 – arxiv.org
Page 1. 1 Deep Reinforcement Learning for Sequence-to-Sequence Models Yaser Keneshloo, Tian Shi, Naren Ramakrishnan, Chandan K. Reddy, Senior Member, IEEE Abstract—In recent times, sequence-to-sequence (seq2seq …
Symbol emergence in cognitive developmental systems: a survey
T Taniguchi, E Ugur, M Hoffmann… – … on Cognitive and …, 2018 – ieeexplore.ieee.org
Page 1. 2379-8920 (c) 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/ redistribution requires IEEE permission. See http://www.ieee.org …