a neural conversation model
play

A Neural Conversation Model Aadil Hayat (13002) Masare Akshay Sunil - PowerPoint PPT Presentation

Oct 09, 2022 •132 likes •249 views

A Neural Conversation Model Aadil Hayat (13002) Masare Akshay Sunil (12403) Problem Statement To create a chatter bot capable to chat in natural language as humans do. DNNs in Conversations The inputs and targets are required to be of

  1. A Neural Conversation Model Aadil Hayat (13002) Masare Akshay Sunil (12403)

  2. Problem Statement • To create a chatter bot capable to chat in natural language as humans do.

  3. DNNs in Conversations • The inputs and targets are required to be of fixed dimensionality • Many problems that deal with sequence of inputs or targets where the dimensionality can not be predicted a-priori • Eg. Speech Recognition, Machine Translation and Question-Answering

  4. Seq2Seq • One LSTM reads a whole Sequence at a time to generate a single vector with a large dimensionality. • The Second LSTM is a RNN which is conditioned on the input sequence.

  5. A Conversational Model • A Translation Model using Seq2Seq will be easier than a conversational model due to no context required. • Input sequence is the concatenation of what has been conversed so far. • Lack of general world knowledge is another limitation of the purely unsupervised model.

  6. IT Helpdesk Troubleshooting VPN Issue

  7. The Dataset • OpenSubtitles dataset Tiedemann 2009. • Movie conversations in XML format. • Training Dataset : 62M sentences (923M tokens) • Validation Dataset : 26M sentences (395 tokens) • Quite large but noisy • This is an open-domain conversation dataset so expected results are quite fascinating.

  8. Cleverbot vs NCM Cleverbot Neural Conversational Model

  9. References [1] Weizenbaum, J. ELIZA - A Computer Program For the Study of Natural Language Communication Between Man And Machine. Communications of the ACM, Vol 9, 1966. [2] Huang, J., Zhou, M., Yang, D. Extracting Chatbot Knowledge from Online Discussion Forums. IJCAI07-066, 2007. [3] Sordoni, A., Galley, M., Auli, M., Brockett, C., Ji, Y., Mitchell, M., Gao, J., Dolan, B., and Nie, J.-Y. A neural network approach to context-sensitive generation of conversational responses. In Proceedings of NAACL, 2015. [4] Sutskever, I., Vinyals, O., and Le, Q. V. Sequence to sequence learning with neural networks. In NIPS, 2014.

  10. References [5] Hochreiter, S. and Schmidhuber, J. Long short-term memory . Neural Computation, 1997. [6] Vinyals, O., Le, Q. V. A Neural Conversational Model. arXiv:1506.05869v3, 2015. [7] Shang, L., Lu, Z., and Li, H. Neural responding machine for short-text conversation. In Proceedings of ACL, 2015. [8] Tiedemann, J. News from OPUS - A collection of multilingual parallel corpora with tools and interfaces. In Nicolov, N., Bontcheva, K., Angelova, G., and Mitkov, R. (eds.), Recent Advances in Natural Language Processing, volume V, pp. 237 – 248. John Benjamins, Amsterdam/Philadelphia, Borovets, Bulgaria, 2009. ISBN 978 90 272 4825 1.

  11. Questions?

Recommend

8 Neural MT 2: Attentional Neural MT In the past chapter, we described a simple model for neural

8 Neural MT 2: Attentional Neural MT In the past chapter, we described a simple model for neural

8 Neural MT 2: Attentional Neural MT In the past chapter, we described a simple model for neural machine translation, which uses an encoder to encode sentences as a fixed-length vector. However, in some ways, this view is overly simplified, and

635 views • 9 slides
Outline Why model neural networks? Modeling Neural Networks A brief look at the neuron.

Outline Why model neural networks? Modeling Neural Networks A brief look at the neuron.

Outline Why model neural networks? Modeling Neural Networks A brief look at the neuron. A look at some current works. Adding an evolutionary strategy. Paul Nuytten CPSC 607 Why Model Neural Networks? The Neuron The nervous

697 views • 4 slides
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation

DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation

DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation EMNLP19 Deepanway Ghosal, Navonil Majumder, Soujanya Poria , Niyati Chhaya and Alexander Gelbukh Singapore University of Technology and Design, Singapore

995 views • 21 slides
Neural Response Ranking for Social Conversation: A Data-Efficient Approach Igor Shalyminov, Ond

Neural Response Ranking for Social Conversation: A Data-Efficient Approach Igor Shalyminov, Ond

Neural Response Ranking for Social Conversation: A Data-Efficient Approach Igor Shalyminov, Ond ej Duek, and Oliver Lemon School of Mathematical and Computer Sciences Heriot-Watt University 31 October 2018 Outline Introduction. Amazon

522 views • 13 slides
Modeling Neural Networks Paul Nuytten CPSC 607 Outline Why model neural networks? A

Modeling Neural Networks Paul Nuytten CPSC 607 Outline Why model neural networks? A

Modeling Neural Networks Paul Nuytten CPSC 607 Outline Why model neural networks? A brief look at the neuron. A look at some current works. Adding an evolutionary strategy. Why Model Neural Networks? The nervous system is a

1.22k views • 23 slides
Grounding Neural Conversation Models into the Real World Michel Galley SCAI October 1 st , 2017

Grounding Neural Conversation Models into the Real World Michel Galley SCAI October 1 st , 2017

Grounding Neural Conversation Models into the Real World Michel Galley SCAI October 1 st , 2017 Inform ormation ation Retri trieval eval Conver nversati sation onal l AI AI Natu Na tura ral l La Languag guage Dialogu ogue Pr

742 views • 30 slides
Exemplar Encoder Decoder for Neural Conversation Generation By Gaurav Pandey, Danish

Exemplar Encoder Decoder for Neural Conversation Generation By Gaurav Pandey, Danish

Exemplar Encoder Decoder for Neural Conversation Generation By Gaurav Pandey, Danish Contractor, Vineet Kumar and Sachindra Joshi IBM Research AI Generative Models for Conversations Context Context Context Response Decoder Embedding

437 views • 12 slides
1 ! Knowing The Right Conversation What would the right conversation look and sound like

1 ! Knowing The Right Conversation What would the right conversation look and sound like

1 ! Knowing The Right Conversation What would the right conversation look and sound like when you initially engage others? If you listened to your colleagues conversation when she or he was dealing with someone what would it reveal?

1.05k views • 70 slides
1 Real Neural Learning Artificial Neuron Model Model network as a graph with cells as nodes

1 Real Neural Learning Artificial Neuron Model Model network as a graph with cells as nodes

Neural Networks Analogy to biological neural systems, the most robust learning systems we know. Attempt to understand natural biological systems CS 391L: Machine Learning through computational modeling. Massive parallelism allows

92 views • 6 slides
Stimulus-Based Conversation Conquering SBC using the SEP model An example of an SBC question a)

Stimulus-Based Conversation Conquering SBC using the SEP model An example of an SBC question a)

Stimulus-Based Conversation Conquering SBC using the SEP model An example of an SBC question a) Look at the road sign. Who do you think it is put up for? Why do you think so? Do you think it is important for drivers to obey the warning on

309 views • 27 slides
Neural Networks + Convolutional Neural Networks Last Class Global Features The perceptron

Neural Networks + Convolutional Neural Networks Last Class Global Features The perceptron

CS4501: Introduction to Computer Vision Neural Networks + Convolutional Neural Networks Last Class Global Features The perceptron model Neural Networks multilayer perceptron model (MLP) Backpropagation Todays Class

789 views • 34 slides
MeanSum : A Neural Model for Unsupervised Multi-Document Abstractive Summarization Eric Chu *

MeanSum : A Neural Model for Unsupervised Multi-Document Abstractive Summarization Eric Chu *

MeanSum : A Neural Model for Unsupervised Multi-Document Abstractive Summarization Eric Chu * Peter J. Liu * MIT Media Lab Google Brain ICML 2019 Text summarization overview Extractive Abstractive Non-neural Human Supervised Neural

317 views • 14 slides
LSTM Neural Reordering Model for Statistical Machine Translation Yiming Cui, Shijin Wang,

LSTM Neural Reordering Model for Statistical Machine Translation Yiming Cui, Shijin Wang,

LSTM Neural Reordering Model for Statistical Machine Translation Yiming Cui, Shijin Wang, Jianfeng Li iFLYTEK Research June 14, 2016 O UTLINE Lexicalized Reordering Model LSTM Neural Reordering Model Experiments & Analyses

423 views • 20 slides
A Generative Model of Group Conversation Hannah Morrison Chris Martens About Me Junior at

A Generative Model of Group Conversation Hannah Morrison Chris Martens About Me Junior at

A Generative Model of Group Conversation Hannah Morrison Chris Martens About Me Junior at North Carolina State University Favorite games: Transistor , Hellblade: Senuas Sacrifice , Life is Strange , Overwatch

546 views • 27 slides
Recurrent Language Models CMSC 470 Marine Carpuat Toward a Neural Language Model Figures by

Recurrent Language Models CMSC 470 Marine Carpuat Toward a Neural Language Model Figures by

Recurrent Language Models CMSC 470 Marine Carpuat Toward a Neural Language Model Figures by Philipp Koehn (JHU) Count-based n-gram models vs. feedforward neural networks Pros of feedforward neural LM Word embeddings capture

547 views • 16 slides
Recurrent Language Models CMSC 470 Marine Carpuat Toward a Neural Language Model Figures by

Recurrent Language Models CMSC 470 Marine Carpuat Toward a Neural Language Model Figures by

Recurrent Language Models CMSC 470 Marine Carpuat Toward a Neural Language Model Figures by Philipp Koehn (JHU) Count-based n-gram models vs. feedforward neural networks Pros of feedforward neural LM Word embeddings capture

279 views • 15 slides
Jeffco Deeper Learning Model November 13, 2019 Conversation Structure Context & current

Jeffco Deeper Learning Model November 13, 2019 Conversation Structure Context & current

Jeffco Deeper Learning Model November 13, 2019 Conversation Structure Context & current state Jeffco Deeper Learning Model Implementation progress Student impact to date MAP data Next steps Task. Predicts.

798 views • 33 slides
Beyond Data and Model Parallelism for Deep Neural Networks ZHIHAO JIA, MATEI ZAHARIA, ALEX AIKEN

Beyond Data and Model Parallelism for Deep Neural Networks ZHIHAO JIA, MATEI ZAHARIA, ALEX AIKEN

Beyond Data and Model Parallelism for Deep Neural Networks ZHIHAO JIA, MATEI ZAHARIA, ALEX AIKEN SYSML 2019 PRESENTED BY JULIUS LISCHEID Existing Parallelisation Approaches (1/2) DATA PARALLELISM MODEL PARALLELISM Replica of neural

284 views • 12 slides
Reimagining Strategy Session 1 1 WELCOME! 2 1 10/20/2020 Our conversation Our conversation

Reimagining Strategy Session 1 1 WELCOME! 2 1 10/20/2020 Our conversation Our conversation

10/20/2020 Reimagining Strategy Session 1 1 WELCOME! 2 1 10/20/2020 Our conversation Our conversation Welcome and Introductions Cohort Overview Strategic Challenges Intended Impact Next Steps 3 The Spectrum Team The

381 views • 14 slides
Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) L1 Scalar Processor L0

Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) L1 Scalar Processor L0

Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) L1 Scalar Processor L0 L0 M*V Processor N N N N N N Project Brainwave Pretrained DNN Model Scalable DNN Hardware Neural Processing Unit Microservice Extract

525 views • 30 slides
Changing the conversation Looking for mechanisms of change in conversation therapies for aphasia

Changing the conversation Looking for mechanisms of change in conversation therapies for aphasia

Changing the conversation Looking for mechanisms of change in conversation therapies for aphasia Fiona Johnson UCL Dept. Language & Communication Guys & St Thomass Community Services Background to research Be$er

530 views • 12 slides
Neural Networks Neural networks arise from attempts to model Neural Networks human/animal

Neural Networks Neural networks arise from attempts to model Neural Networks human/animal

Feed-forward Networks Network Training Error Backpropagation Deep Learning Feed-forward Networks Network Training Error Backpropagation Deep Learning Neural Networks Neural networks arise from attempts to model Neural Networks

380 views • 9 slides
Neural Information Retrieval Wassila Lalouani 1 Plan Neural network architectures Neural

Neural Information Retrieval Wassila Lalouani 1 Plan Neural network architectures Neural

Neural Information Retrieval Wassila Lalouani 1 Plan Neural network architectures Neural IR tasks Neural IR architecture Feature Representations Neural IR query auto completion Neural IR query suggestion Neural IR document

1.48k views • 18 slides
Join the Conversation on Twitter Use #AMSSAevents to follow the conversation on Twitter and

Join the Conversation on Twitter Use #AMSSAevents to follow the conversation on Twitter and

Join the Conversation on Twitter Use #AMSSAevents to follow the conversation on Twitter and connect with other webinar participants. AMSSA can be found on Twitter @amssabc Tim Foran Immigration, Refugees and Citizenship Canada (IRCC)

1.53k views • 116 slides

More recommend