Question Answering System

Slides:



Advertisements
Similar presentations
Introduction to Recurrent neural networks (RNN), Long short-term memory (LSTM) Wenjie Pei In this coffee talk, I would like to present you some basic.
Advertisements

Deep Learning Neural Network with Memory (1)
Addressing the Rare Word Problem in Neural Machine Translation
Haitham Elmarakeby.  Speech recognition
Convolutional LSTM Networks for Subcellular Localization of Proteins
Predicting the dropouts rate of online course using LSTM method
NOTE: To change the image on this slide, select the picture and delete it. Then click the Pictures icon in the placeholder to insert your own image. SHOW.
Xintao Wu University of Arkansas Introduction to Deep Learning 1.
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation EMNLP’14 paper by Kyunghyun Cho, et al.
A Sentence Interaction Network for Modeling Dependence between Sentences Biao Liu, Minlie Huang Tsinghua University.
Convectional Neural Networks
Attention Model in NLP Jichuan ZENG.
Deep Learning RUSSIR 2017 – Day 3
Convolutional Sequence to Sequence Learning
Unsupervised Learning of Video Representations using LSTMs
Deep Learning Methods For Automated Discourse CIS 700-7
SD Study RNN & LSTM 2016/11/10 Seitaro Shinagawa.
End-To-End Memory Networks
CS 388: Natural Language Processing: LSTM Recurrent Neural Networks
CS 4501: Introduction to Computer Vision Computer Vision + Natural Language Connelly Barnes Some slides from Fei-Fei Li / Andrej Karpathy / Justin Johnson.
Recurrent Neural Networks for Natural Language Processing
Van-Khanh Tran and Le-Minh Nguyen
Recurrent Neural Networks
Neural Machine Translation by Jointly Learning to Align and Translate
Show and Tell: A Neural Image Caption Generator (CVPR 2015)
ICS 491 Big Data Analytics Fall 2017 Deep Learning
Generating Natural Answers by Incorporating Copying and Retrieving Mechanisms in Sequence-to-Sequence Learning Shizhu He, Cao liu, Kang Liu and Jun Zhao.
Neural Machine Translation By Learning to Jointly Align and Translate
Using Recurrent Neural Networks and Hebbian Learning
Attention Is All You Need
Master’s Thesis defense Ming Du Advisor: Dr. Yi Shang
Lecture 23: More on Word Embeddings
Advanced Recurrent Architectures
A critical review of RNN for sequence learning Zachary C
Grid Long Short-Term Memory
RNN and LSTM Using MXNet Cyrus M Vahid, Principal Solutions Architect
Advanced Artificial Intelligence
Paraphrase Generation Using Deep Learning
Image Captions With Deep Learning Yulia Kogan & Ron Shiff
Final Presentation: Neural Network Doc Summarization
Understanding LSTM Networks
ECE599/692 - Deep Learning Lecture 14 – Recurrent Neural Network (RNN)
Recurrent Encoder-Decoder Networks for Time-Varying Dense Predictions
Machine Translation(MT)
Natural Language to SQL(nl2sql)
Report by: 陆纪圆.
Attention.
Ali Hakimi Parizi, Paul Cook
Please enjoy.
LSTM: Long Short Term Memory
Meta Learning (Part 2): Gradient Descent as LSTM
Deep Learning Authors: Yann LeCun, Yoshua Bengio, Geoffrey Hinton
Attention for translation
Recurrent Neural Networks (RNNs)
Automatic Handwriting Generation
The experiments based on Recurrent Neural Networks
Baseline Model CSV Files Pandas DataFrame Sentence Lists
Attention Is All You Need
Recurrent Neural Networks
Sequence-to-Sequence Models
Bidirectional LSTM-CRF Models for Sequence Tagging
LHC beam mode classification
Neural Machine Translation by Jointly Learning to Align and Translate
Listen Attend and Spell – a brief introduction
CRCV REU 2019 Week 4.
A Neural Network for Car-Passenger matching in Ride Hailing Services.
CRCV REU 2019 Aaron Honculada.
Andrew Karl, Ph.D. James Wisnowski, Ph.D. Lambros Petropoulos
The experiment based on hier-attention
Presentation transcript:

Question Answering System Alireza Torabian

Content Conclusion QA System Neural References Network Human Resource Problems Conclusion QA System Neural Network References

QA System QA System HR Problems Neural Network Conclusion References 1

QA System ▶︎ Auto Responder with Normal Human Language ▶︎ Answer based on Knowledge Bases ▶︎ Information Retrieval Technology and Natural Language Processing QA System HR Problems Neural Network Conclusion References 2

QA System QA System HR Problems Neural Network Conclusion References 3

Human Resource Problems Low Speed Lack of Full-time HR High Cost Fatigue QA System HR Problems Neural Network Conclusion References 4

5

Neural Network ▶︎ Inspired by the natural NN ▶︎ Huge number of neurons ▶︎ Machine learning methods ▶︎ Predict output ▶︎ Inspired by the natural NN ▶︎ Huge number of neurons QA System HR Problems Neural Network Conclusion References 6

Recurrent Neural Network (RNN) QA System HR Problems Neural Network Conclusion References 7

LSTM RNN Cell LSTM Cell 8 QA System HR Problems Neural Network Conclusion References 8

LSTM Information (State) Transmission Chain 9 QA System HR Problems Neural Network Conclusion References 9

LSTM New input effect 10 QA System HR Problems Neural Network Conclusion References 10

LSTM Calculate new information 11 QA System HR Problems Neural Network Conclusion References 11

LSTM Forget last and add new information 12 QA System HR Problems Neural Network Conclusion References 12

LSTM Calculate output 13 QA System HR Problems Neural Network Conclusion References 13

Seq2Seq Model Encoder Decoder 14 <END> QA System HR Problems Neural Network Conclusion References 14

Attention Mechanism LSTM Bidirectional LSTM 15 w1 w2 w3 w4 w5 QA System HR Problems Neural Network Conclusion References 15

Conclusion ▶︎ RNN ▶︎ Sequence to Sequence Model ▶︎ Automated QA System ▶︎ Need QA Systems ▶︎ RNN ▶︎ Sequence to Sequence Model ▶︎ Attention Mechanism QA System HR Problems Neural Network Conclusion References 16

References [1] A. Andrenucci, E. Sneiders, “Automated question answering: review of the main approaches” (2005) [2] Ilya Sutskever, Oriol Vinyals, Quoc V. Le, “Sequence to Sequence Learning with Neural Networks” (2014) [3] https://towardsdatascience.com/introduction-to-sequence-models-rnn-bidirectional-rnn-lstm-gru-73927ec9df15 [4] https://github.com/ematvey/tensorflow-seq2seq-tutorials/blob/master/1-seq2seq.ipynb [5] http://colah.github.io/posts/2015-08-Understanding-LSTMs/ QA System HR Problems Neural Network Conclusion References 17

Thank You