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        <title>Methods and Theories for Large-scale Structured Prediction</title>
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            <title>Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts</title>
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            <namePart type="given">Bishan</namePart>
            <namePart type="family">Yang</namePart>
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        <name type="personal">
            <namePart type="given">Rebecca</namePart>
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    <abstract>Many important NLP tasks are casted as structured prediction problems, and try to predict certain forms of structured output from the input. Examples of structured prediction include POS tagging, named entity recognition, PCFG parsing, dependency parsing, machine translation, and many others. When apply structured prediction to a specific NLP task, there are the following challenges:1. Model selection: Among various models/algorithms with different characteristics, which one should we choose for a specific NLP task?2. Training: How to train the model parameters effectively and efficiently?3. Overfitting: To achieve good accuracy on test data, it is important to control the overfitting from the training data. How to control the overfitting risk for structured prediction?This tutorial will provide a clear overview of recent advances in structured prediction methods and theories, and address the above issues when we apply structured prediction to NLP tasks. We will introduce large margin methods (e.g., perceptrons, MIRA), graphical models (e.g., CRFs), and deep learning methods (e.g., RNN, LSTM), and show the respective advantages and disadvantages for NLP applications. For the training algorithms, we will introduce online/ stochastic training methods, and we will introduce parallel online/stochastic learning algorithms and theories to speed up the training (e.g., the Hogwild algorithm). For controlling the overfitting from training data, we will introduce the weight regularization methods, structure regularization, and implicit regularization methods.</abstract>
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%0 Conference Proceedings
%T Methods and Theories for Large-scale Structured Prediction
%A Sun, Xu
%A Feng, Yansong
%Y Yang, Bishan
%Y Hwa, Rebecca
%S Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
%D 2016
%8 November
%I Association for Computational Linguistics
%C Austin, Texas
%F sun-feng-2016-methods
%X Many important NLP tasks are casted as structured prediction problems, and try to predict certain forms of structured output from the input. Examples of structured prediction include POS tagging, named entity recognition, PCFG parsing, dependency parsing, machine translation, and many others. When apply structured prediction to a specific NLP task, there are the following challenges:1. Model selection: Among various models/algorithms with different characteristics, which one should we choose for a specific NLP task?2. Training: How to train the model parameters effectively and efficiently?3. Overfitting: To achieve good accuracy on test data, it is important to control the overfitting from the training data. How to control the overfitting risk for structured prediction?This tutorial will provide a clear overview of recent advances in structured prediction methods and theories, and address the above issues when we apply structured prediction to NLP tasks. We will introduce large margin methods (e.g., perceptrons, MIRA), graphical models (e.g., CRFs), and deep learning methods (e.g., RNN, LSTM), and show the respective advantages and disadvantages for NLP applications. For the training algorithms, we will introduce online/ stochastic training methods, and we will introduce parallel online/stochastic learning algorithms and theories to speed up the training (e.g., the Hogwild algorithm). For controlling the overfitting from training data, we will introduce the weight regularization methods, structure regularization, and implicit regularization methods.
%U https://aclanthology.org/D16-2006

Markdown (Informal)

[Methods and Theories for Large-scale Structured Prediction](https://aclanthology.org/D16-2006) (Sun & Feng, EMNLP 2016)

• Methods and Theories for Large-scale Structured Prediction (Sun & Feng, EMNLP 2016)

ACL

• Xu Sun and Yansong Feng. 2016. Methods and Theories for Large-scale Structured Prediction. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts, Austin, Texas. Association for Computational Linguistics.