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at COLING 2022
Overview
Overview
Accurate estimates of uncertainty are important for many difficult or sensitive prediction tasks in natural language processing (NLP). Though large-scale pre-trained models have vastly improved the accuracy of applied machine learning models throughout the field, there still are many instances in which they fail. The ability to precisely quantify uncertainty while handling the challenging scenarios that modern models can face when deployed in the real world is critical for reliable, consequential-decision making. This tutorial is intended for both academic researchers and industry practitioners alike, and provides a comprehensive introduction to uncertainty estimation for NLP problems---from fundamentals in probability calibration, Bayesian inference, and confidence set (or interval) construction, to applied topics in modern out-of-distribution detection and selective inference.
Speakers
Speakers
Outline
Outline
(1) Introduction
Understanding uncertainty.
How do we express it? Use it?
Examples in NLP applications.
(2) Probability Calibration
A frequentist definition.
Measuring calibration.
Simple re-calibration methods.
(3) Bayesian Approaches
Probabilistic models.
Bayesian NNs, ensembles &Â dropout.
Uses in active learning.
(4) Conformal Prediction
Set-valued predictions with guarantees.
Nonconformity scores to sets.
Extensions and applications.
(5) Selective Prediction & OOD Detection
Choosing to abstain.
Training selection mechanisms.
Distinguishing in-domain vs. out-domain.
(6) Conclusion
Review of core concepts.
Different views for uncertainty.
Active areas of relevant research.
Slides
Slides
Citation
Citation
@inproceedings{51177,
title = {Uncertainty Estimation for Natural Language Processing},
author = {Adam Fisch and Robin Jia and Tal Schuster},
year = {2022},
booktitle = {COLING}
}
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