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Intro
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A Prediction Problem
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Types of Prediction
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Why Call it "Structured" Prediction?
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Many Varieties of Structured Prediction!
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Sequence Labeling as
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Sequence Labeling w
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Why Model Interactions in Output? . Consistency is important!
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A Tagger Considering Output Structure
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Training Structured Models
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Local Normalization and
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The Structured Perceptron Algorithm . An extremely simple way of training (non-probabilistic) global models
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Structured Perceptron Loss
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Contrasting Perceptron and Global Normalization • Globally normalized probabilistic model
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Structured Training and Pre-training
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Cost-Augmented Decoding for Hamming Loss • Hamming loss is decomposable over each word • Solution: add a score - Cost to each incorrect choice during search
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What's Wrong w/ Structured Hinge Loss?
Description:
Explore structured prediction basics in this lecture from CMU's Neural Networks for NLP course. Delve into the Structured Perceptron algorithm, structured max-margin objectives, and simple remedies to exposure bias. Learn about various types of prediction, the importance of modeling output interactions, and training methods for structured models. Examine local normalization, global normalization, and cost-augmented decoding for Hamming loss. Gain insights into sequence labeling, tagger considerations for output structure, and the challenges associated with structured hinge loss.

Neural Nets for NLP - Structured Prediction Basics

Graham Neubig
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#Computer Science #Artificial Intelligence #Neural Networks #Natural Language Processing (NLP) #Machine Learning #Model Training #Sequence Labeling
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Intro