Quantifying Uncertainties in Natural Language Processing Tasks

  • Yijun Xiao University of California, Santa Barbara
  • William Yang Wang University of California, Santa Barbara

Abstract

Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems. Recent progress in Bayesian deep learning has made such quantification realizable. In this paper, we propose novel methods to study the benefits of characterizing model and data uncertainties for natural language processing (NLP) tasks. With empirical experiments on sentiment analysis, named entity recognition, and language modeling using convolutional and recurrent neural network models, we show that explicitly modeling uncertainties is not only necessary to measure output confidence levels, but also useful at enhancing model performances in various NLP tasks.

Published
2019-07-17
Section
AAAI Technical Track: Natural Language Processing