Quantifying Uncertainties in Natural Language Processing Tasks
This addresses the need for explainable and reliable AI systems in natural language processing, though it is incremental as it builds on existing Bayesian deep learning methods.
The paper tackled the problem of uncertainty quantification in NLP tasks, showing that explicitly modeling uncertainties improves model performance across sentiment analysis, named entity recognition, and language modeling.
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.