structured perceptron margin methods
play

Structured Perceptron/ Margin Methods Graham Neubig Site - PowerPoint PPT Presentation

Jul 02, 2023 •312 likes •603 views

CS11-747 Neural Networks for NLP Structured Perceptron/ Margin Methods Graham Neubig Site https://phontron.com/class/nn4nlp2020/ Types of Prediction Two classes ( binary classification ) positive I hate this movie negative

  1. CS11-747 Neural Networks for NLP Structured Perceptron/ Margin Methods Graham Neubig Site https://phontron.com/class/nn4nlp2020/

  2. Types of Prediction • Two classes ( binary classification ) positive I hate this movie negative • Multiple classes ( multi-class classification ) very good good I hate this movie neutral bad very bad • Exponential/infinite labels ( structured prediction ) I hate this movie PRP VBP DT NN I hate this movie kono eiga ga kirai

  3. Many Varieties of Structured Prediction! • Models: • RNN-based decoders Covered • Convolution/self attentional decoders already • CRFs w/ local factors • Training algorithms: • Maximum likelihood w/ teacher forcing • Sequence level likelihood Covered • Structured perceptron, structured large margin today • Reinforcement learning/minimum risk training • Sampling corruptions of data

  4. <latexit sha1_base64="OZ0fwiGra8uyR0OpSCMUh2f+CTQ=">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</latexit> <latexit sha1_base64="OZ0fwiGra8uyR0OpSCMUh2f+CTQ=">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</latexit> <latexit sha1_base64="OZ0fwiGra8uyR0OpSCMUh2f+CTQ=">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</latexit> Reminder: Globally Normalized Models • Locally normalized models: each decision made by the model has a probability that adds to one | Y | e S ( y j | X,y 1 ,...,y j − 1 ) Y P ( Y | X ) = y j ∈ V e S (˜ y j | X,y 1 ,...,y j − 1 ) P ˜ j =1 • Globally normalized models (a.k.a. energy- based models): each sentence has a score, which is not normalized over a particular decision e S ( X,Y ) P ( Y | X ) = Y ∈ V ∗ e S ( X, ˜ Y ) P ˜

  5. Globally Normalized Likelihood

  6. <latexit sha1_base64="OZ0fwiGra8uyR0OpSCMUh2f+CTQ=">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</latexit> <latexit sha1_base64="OZ0fwiGra8uyR0OpSCMUh2f+CTQ=">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</latexit> <latexit sha1_base64="OZ0fwiGra8uyR0OpSCMUh2f+CTQ=">ACNnicbVBSxtBGJ3VamO0GvXoZWgQEilhV4S2B0H04klS2piEbAyzs9/qkJnZWZWCMP+Ky/+DW968aDitT+hkxikTfpg4PHe+/jme1HGmTa+f+8tLH5YWv5YWimvrn1a36hsbp3rNFcUWjTlqepERANnElqGQ6dTAEREYd2NDwZ+1rUJql8pcZdAX5FKyhFinDSonDVrXRwKFuNOHR/iMFGEWriw+Get8wV360VhQ52LgQ0N4zHYboFDJvH5XvEendceFCp+g1/AjxPgimpoimag8pdGKc0FyAN5UTrXuBnpm+JMoxyKMphriEjdEguoeoJAJ0307uLvCuU2KcpMo9afBE/XvCEqH1SEQuKYi50rPeWPyf18tN8q1vmcxyA5K+LUpyjk2KxyXimCmgho8cIVQx91dMr4irzriqy6EYPbkedLab3xvBD8OqkfH0zZKaAd9RjUoK/oCJ2iJmohim7QA3pCz96t9+i9eK9v0QVvOrON/oH3+w9wk6ow</latexit> Difficulties Training Globally Normalized Models • Partition function problematic e S ( X,Y ) P ( Y | X ) = Y ∈ V ∗ e S ( X, ˜ Y ) P ˜ • Two options for calculating partition function • Structure model to allow enumeration via dynamic programming, e.g. linear chain CRF, CFG • Estimate partition function through sub-sampling hypothesis space

  7. Two Methods for Approximation • Sampling: • Sample k samples according to the probability distribution • + Unbiased estimator: as k gets large will approach true distribution • - High variance: what if we get low-probability samples? • Beam search: • Search for k best hypotheses • - Biased estimator: may result in systematic differences from true distribution • + Lower variance: more likely to get high-probability outputs

  8. Un-normalized Models: Structured Perceptron

Recommend

Machine Learning Fall 2017 Structured Prediction (structured perceptron, HMM, structured SVM)

Machine Learning Fall 2017 Structured Prediction (structured perceptron, HMM, structured SVM)

Machine Learning Fall 2017 Structured Prediction (structured perceptron, HMM, structured SVM) Professor Liang Huang (Chap. 17 of CIML) Structured Prediction x x the man bit the dog the man bit the dog x x DT NN

672 views • 27 slides
Predicting Sequences: Structured Perceptron CS 6355: Structured Prediction 1 Conditional Random

Predicting Sequences: Structured Perceptron CS 6355: Structured Prediction 1 Conditional Random

Predicting Sequences: Structured Perceptron CS 6355: Structured Prediction 1 Conditional Random Fields summary An undirected graphical model Decompose the score over the structure into a collection of factors Each factor assigns a

865 views • 47 slides
lti Is convex Perceptron Boosting Max-Margin Conditional Likelihood MIRA Based on Uses a

lti Is convex Perceptron Boosting Max-Margin Conditional Likelihood MIRA Based on Uses a

Softmax-Margin CRFs: Training Log-Linear Models with Cost Functions Kevin Gimpel and Noah A. Smith lti Is convex Perceptron Boosting Max-Margin Conditional Likelihood MIRA Based on Uses a cost probabilistic function inference

603 views • 26 slides
Structured Perceptron CMSC 470 Marine Carpuat POS tagging Sequence labeling with the perceptron

Structured Perceptron CMSC 470 Marine Carpuat POS tagging Sequence labeling with the perceptron

Sequence Labeling with the Structured Perceptron CMSC 470 Marine Carpuat POS tagging Sequence labeling with the perceptron Sequence labeling problem Structured Perceptron Input: Perceptron algorithm can be used for sequence labeling

654 views • 13 slides
Structured Perceptron with Inexact Search x x the man bit the dog x the man bit

Structured Perceptron with Inexact Search x x the man bit the dog x the man bit

Structured Perceptron with Inexact Search x x the man bit the dog x the man bit the dog x DT NN VBD DT NN y y y=+ 1 y=- 1 Liang Huang Suphan Fayong Yang Guo Information Sciences Institute

1.15k views • 100 slides
Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction Hal

Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction Hal

Hal Daum III (hdaume@isi.edu) Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction Hal Daum III and Daniel Marcu Information Sciences Institute University of Southern California {hdaume,marcu}@isi.edu

400 views • 20 slides
NLP Programming Tutorial 11 - The Structured Perceptron Graham Neubig Nara Institute of Science

NLP Programming Tutorial 11 - The Structured Perceptron Graham Neubig Nara Institute of Science

NLP Programming Tutorial 11 The Structured Perceptron NLP Programming Tutorial 11 - The Structured Perceptron Graham Neubig Nara Institute of Science and Technology (NAIST) 1 NLP Programming Tutorial 11 The Structured Perceptron

447 views • 31 slides
Sequential Data Modeling - The Structured Perceptron Graham Neubig Nara Institute of Science and

Sequential Data Modeling - The Structured Perceptron Graham Neubig Nara Institute of Science and

Sequential Data Modeling The Structured Perceptron Sequential Data Modeling - The Structured Perceptron Graham Neubig Nara Institute of Science and Technology (NAIST) 1 Sequential Data Modeling The Structured Perceptron Prediction

604 views • 46 slides
Large Margin Classification Using the Perceptron Algorithm (Part 2) Henry Tan Georgetown

Large Margin Classification Using the Perceptron Algorithm (Part 2) Henry Tan Georgetown

Large Margin Classification Using the Perceptron Algorithm (Part 2) Henry Tan Georgetown University April 13, 2015 Georgetown University Perceptron 1 Analysis - Theorem 3 Theorem 3 Assume all examples ( x , y ) are generated i.i.d. Let

494 views • 31 slides
TAG, Dynamic Programming, and the Perceptron for Efficent, Feature-Rich Parsing Xavier Carreras,

TAG, Dynamic Programming, and the Perceptron for Efficent, Feature-Rich Parsing Xavier Carreras,

TAG, Dynamic Programming, and the Perceptron for Efficent, Feature-Rich Parsing Xavier Carreras, Michael Collins and Terry Koo MIT CSAIL Discriminative Models for Parsing Structured Prediction methods like CRF or Perceptron train linear models

403 views • 27 slides
Scalable Large-Margin x x the man bit the dog the man bit the dog x x

Scalable Large-Margin x x the man bit the dog the man bit the dog x x

What is Structured Prediction? Scalable Large-Margin x x the man bit the dog the man bit the dog x x Structured Learning: DT NN VBD DT NN S y Theory and Algorithms NP VP y=+ 1 y=- 1 the man bit the

701 views • 22 slides
CS 472 - Perceptron 1 Basic Neuron CS 472 - Perceptron 2 Expanded Neuron CS 472 - Perceptron

CS 472 - Perceptron 1 Basic Neuron CS 472 - Perceptron 2 Expanded Neuron CS 472 - Perceptron

CS 472 - Perceptron 1 Basic Neuron CS 472 - Perceptron 2 Expanded Neuron CS 472 - Perceptron 3 Perceptron Learning Algorithm l First neural network learning model in the 1960s l Simple and limited (single layer models) l Basic concepts

571 views • 53 slides
Part-of-Speech T agging: HMM &amp; structured perceptron CMSC 723 / LING 723 / INST 725 M ARINE

Part-of-Speech T agging: HMM &amp; structured perceptron CMSC 723 / LING 723 / INST 725 M ARINE

Part-of-Speech T agging: HMM &amp; structured perceptron CMSC 723 / LING 723 / INST 725 M ARINE C ARPUAT marine@cs.umd.edu Last time What are parts of speech (POS)? Equivalence classes or categories of words Open class vs.

1.06k views • 61 slides
Joint Event Trigger Identification and Event Coreference Resolution with Structured Perceptron

Joint Event Trigger Identification and Event Coreference Resolution with Structured Perceptron

Joint Event Trigger Identification and Event Coreference Resolution with Structured Perceptron Jun Araki and Teruko Mitamura Language Technologies Institute School of Computer Science Carnegie Mellon University September 21, 2015 EMNLP 2015

204 views • 17 slides
Loss-augmented Structured Prediction CMSC 723 / LING 723 / INST 725 Marine Carpuat Figures,

Loss-augmented Structured Prediction CMSC 723 / LING 723 / INST 725 Marine Carpuat Figures,

Loss-augmented Structured Prediction CMSC 723 / LING 723 / INST 725 Marine Carpuat Figures, algorithms &amp; equations from CIML chap 17 POS tagging Sequence labeling with the perceptron Sequence labeling problem Structured Perceptron

577 views • 40 slides
Structured-Cut: A Max-Margin Feature Selection Framework for Video Segmentation Nikhil S. Naikal

Structured-Cut: A Max-Margin Feature Selection Framework for Video Segmentation Nikhil S. Naikal

Structured-Cut: A Max-Margin Feature Selection Framework for Video Segmentation Nikhil S. Naikal Berkeley EECS Abstract precisely tracking the contour of the object in the 2D image pro- jections. A given video sequence can easily exhibit

56 views • 5 slides
A STRUCTURED L IFE A STRUCTURED L IFE A STRUCTURED L IFE A STRUCTURED L IFE A STRUCTURED L IFE

A STRUCTURED L IFE A STRUCTURED L IFE A STRUCTURED L IFE A STRUCTURED L IFE A STRUCTURED L IFE

A STRUCTURED L IFE A STRUCTURED L IFE A STRUCTURED L IFE A STRUCTURED L IFE A STRUCTURED L IFE by Kirk Gittings Note: Kirk Gittings has been photographing the prehistor- coast, but m aking a real living as an art photographer ic,

1.16k views • 8 slides
23 Action-Oriented Design Methods 1. Use Cases 2. Structured Analysis/Design (SA/SD) 3. Structured

23 Action-Oriented Design Methods 1. Use Cases 2. Structured Analysis/Design (SA/SD) 3. Structured

Fakultt Informatik, Institut fr Software- und Multimediatechnik, Lehrstuhl fr Softwaretechnologie 23 Action-Oriented Design Methods 1. Use Cases 2. Structured Analysis/Design (SA/SD) 3. Structured Analysis and Design Technique (SADT)

528 views • 32 slides
26. Data-Oriented Design Methods 1) Jackson Structured Programming (JSP) and Jackson Structured

26. Data-Oriented Design Methods 1) Jackson Structured Programming (JSP) and Jackson Structured

Fakultt Informatik - Institut Software- und Multimediatechnik - Softwaretechnologie Prof. Amann - Softwaretechnologie II 26. Data-Oriented Design Methods 1) Jackson Structured Programming (JSP) and Jackson Structured Diagrams (JSD)

324 views • 20 slides
Structured Prediction Problem Unstructured prediction Structured prediction Part of

Structured Prediction Problem Unstructured prediction Structured prediction Part of

Laplace Max-margin Markov Networks Recent Recent A Advances in dvances in L Learning earning SPARSE SPARSE Structured S tructured I I/ /O O M Model odels s: : models, algorithms, and applications models, algorithms, and

629 views • 24 slides
more tasks, more methods CMSC 470 Marine Carpuat Recap: We know how to perform POS tagging with

more tasks, more methods CMSC 470 Marine Carpuat Recap: We know how to perform POS tagging with

Sequence Labeling: more tasks, more methods CMSC 470 Marine Carpuat Recap: We know how to perform POS tagging with structured perceptron An example of sequence labeling tasks Requires a predefined set of POS tags Penn Treebank

396 views • 21 slides
How to Train Your Perceptron 16-385 Computer Vision (Kris Kitani) Carnegie Mellon University

How to Train Your Perceptron 16-385 Computer Vision (Kris Kitani) Carnegie Mellon University

PERCEPTRON How to Train Your Perceptron 16-385 Computer Vision (Kris Kitani) Carnegie Mellon University Lets start easy worlds smallest perceptron! w f y x y = wx (a.k.a. line equation, linear regression) Learning a Perceptron

656 views • 20 slides
The Perceptron Algorithm Machine Learning 1 Some slides based on lectures from Dan Roth, Avrim

The Perceptron Algorithm Machine Learning 1 Some slides based on lectures from Dan Roth, Avrim

The Perceptron Algorithm Machine Learning 1 Some slides based on lectures from Dan Roth, Avrim Blum and others Outline The Perceptron Algorithm Variants of Perceptron Perceptron Mistake Bound 2 Where are we? The Perceptron

559 views • 44 slides
Structured sparse methods for matrix factorization Francis Bach Sierra team, INRIA - Ecole

Structured sparse methods for matrix factorization Francis Bach Sierra team, INRIA - Ecole

Structured sparse methods for matrix factorization Francis Bach Sierra team, INRIA - Ecole Normale Sup erieure March 2011 Joint work with R. Jenatton, J. Mairal, G. Obozinski Structured sparse methods for matrix factorization Outline

1.07k views • 73 slides

More recommend