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Quantifying Uncertainties in Natural Language Processing Tasks

Yijun Xiao, William Yang Wang

arXiv:1811.07253v1cs.CLcs.AIcs.LGcs.NE

TL;DR

NLP models need uncertainty estimates because accurate black-box predictions can lack confidence information. The paper defines and quantifies model and data uncertainties across three NLP tasks, finding improved performance and higher data uncertainty for harder predictions. It also notes a classification-setting limitation in the Gaussian assumption used for regression.

  • Problem

    Black-box NLP models need to quantify prediction confidence, because satisfactory overall performance does not reveal confidence for individual inputs.

  • Method

    The paper defines model and data uncertainties, then evaluates them with convolutional and recurrent neural-network models on sentiment analysis, named entity recognition, and language modeling.

  • Results

    Accounting for both uncertainties improves performance across all three NLP tasks, including up to 26.4% on sentiment analysis, 2.7% F1 on named entity recognition, and 4.2% perplexity improvement in language modeling.

  • Takeaways & Limitations

    Higher estimated data uncertainty for more difficult predictions indicates that the quantified uncertainty measures track prediction difficulty in sentiment analysis and named entity recognition.

  • Takeaways & Limitations

    The Gaussian assumption used for the regression formulation does not hold in classification, so the paper adapts the formulation in logit space.

Abstract

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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.

Introduction

The paper argues that NLP models should quantify both model and data uncertainties, then studies their benefits across three tasks. It defines these uncertainties and reports improved performance and higher data uncertainty for harder predictions.

  • Motivation: Uncertainty quantification is needed because black-box models may be accurate overall while providing no confidence estimate for individual predictions.The paper links this limitation to AI safety and social-bias concerns.
  • Uncertainty Types: Model uncertainty concerns whether model structure and parameters adequately describe the data distribution, whereas data uncertainty arises from noise in the data-generation process.The paper distinguishes input-independent homoscedastic noise from input-dependent heteroscedastic noise.
  • Method: Bayesian neural networks represent model weights as probability distributions, enabling model-uncertainty estimation through repeated weight sampling and forward passes.The paper also associates this approach with potentially better representations and predictions through the ensemble nature of Bayesian neural networks.
  • Study Scope: The study evaluates model and input-dependent data uncertainty in sentiment analysis, named entity recognition, and language modeling.Its stated goal is to examine both performance effects and the characteristics of quantified uncertainties.
  • Contributions: Accounting for model and data uncertainties yields significant improvements across the three NLP tasks, while higher data uncertainty appears for more difficult predictions.The paper reports these as its main empirical findings and analyzes the quantified uncertainty characteristics.

Related Work

Related work frames uncertainty estimation through Bayesian neural networks and approximate inference, while distinguishing model uncertainty from noise inherent in the data distribution. Prior studies applied related approaches beyond this paper's NLP focus.

  • Bayesian Neural Networks: Bayesian neural networks place a prior over network parameters and infer a posterior distribution rather than a single point estimate.The posterior describes possible model-weight values given the dataset, and predictions are obtained by marginalizing over those weights.
  • Approximate Inference: Because exact Bayesian neural-network inference is rarely available for modern networks, Monte Carlo dropout provides an approximate method using repeated stochastic forward passes.The paper describes dropout at test time as a variational Bayesian approximation and estimates model uncertainty from output variance.
  • Uncertainty Types: Model uncertainty captures uncertainty about parameters, whereas data uncertainty captures noise in the data distribution and may require heteroscedastic models when noise varies with the input.Homogeneous noise can instead be modeled as a parameter.
  • Prior Applications: Earlier studies investigated uncertainty in semantic segmentation, recurrent language modeling, sentiment analysis, vision, time-series prediction, and anomaly detection.This study focuses specifically on the benefits of quantifying model and data uncertainties in NLP tasks with common neural-network structures.

Methods

The methods decompose predictive variance into model and data uncertainty, then estimate both with Bayesian inference, learned input-dependent variance, and Monte Carlo evaluation. The study applies these components across regression and classification settings, including logit-space modeling for classification.

  • Uncertainty decomposition: Predictive variance is decomposed into model uncertainty from the mapping process and data uncertainty from variation in the conditional distribution.Both components jointly explain different parts of observation noise in y.
  • Model uncertainty: Bayesian neural networks represent posterior uncertainty over weights, with variational inference approximating the intractable posterior distribution.The approximation minimizes KL divergence between the variational distribution and the true posterior.
  • Model uncertainty: MC dropout estimates model uncertainty by activating dropout at test time to sample from an approximate posterior with minimal model changes.The study uses these samples for prediction and predictive-variance approximation.
  • Data uncertainty: Input-dependent data uncertainty is modeled by predicting both output values and their variances, thereby estimating Var(y|x).The model uses functions parameterized by W to calculate output means and standard deviations for each input.
  • Data uncertainty: Learned loss attenuation increases estimated variance for inputs whose predicted means deviate more from observations while regularizing variance estimates.This formulation is used for regression under a Gaussian observation assumption.
  • Data uncertainty: For classification, data uncertainty is modeled in logit space, sampled logits are transformed with softmax, and average logit variance serves as a surrogate.The regression Gaussian assumption does not directly hold for classification, so the formulation is adapted to logits.

Experiments and Results

The paper evaluates uncertainty-aware CNN and LSTM models on sentiment analysis, named entity recognition, and language modeling. Accounting for uncertainties improves performance across all three tasks, with task-specific differences in which uncertainty helps most.

  • Experimental scope: Experiments cover sentiment analysis, named entity recognition, and language modeling using CNN and LSTM models.Sentiment analysis includes regression and classification settings; NER uses a bidirectional LSTM, and language modeling uses a two-layer LSTM.
  • Sentiment Analysis: Sentiment regression evaluates CNN models with mean squared error on four large-scale review datasets.The study uses IMDB and Yelp review datasets, with accuracy used for classification and MSE for regression.
  • Sentiment Analysis: 13.5%-26.4%: quantifying both model and data uncertainties improves sentiment regression performance over non-Bayesian baselines.Most of the gain comes from model uncertainty, while input-dependent uncertainty alone marginally hurts regression performance; classification gains are marginal.
  • Named Entity Recognition: 2.7%: input-dependent data uncertainty improves NER F1, whereas model uncertainty under-performs by approximately 1% absolute F1.NER uses a bidirectional LSTM with dropout masks shared across time steps; evaluation requires exact entity matches.
  • Language Modeling: 4.2%: language-model perplexity improves when both model and data uncertainties are quantified.The experiment uses a two-layer LSTM on Penn Treebank with shared dropout masks across time steps and 50 MC-dropout samples.
  • Summary of Results: Accounting for uncertainties improves model performance across all three evaluated NLP tasks.The paper reports up to 26.4% improvement for sentiment analysis, 2.7% higher NER F1, and 4.2% better language-model perplexity.

Analysis

The analysis examines how quantified data uncertainty relates to prediction difficulty in sentiment analysis and named entity recognition. Higher uncertainty tracks inputs and tags that are harder to predict.

  • Data uncertainty: Input-dependent data uncertainty is high when an input makes its corresponding output difficult to predict.The analysis focuses mainly on data uncertainty while noting similar observations for model uncertainty.
  • Sentiment analysis: High-uncertainty Yelp examples are either short or very long with extensive action descriptions, whereas low-uncertainty examples are medium-length and contain strong opinion tokens.
  • Named entity recognition: NER token difficulty is measured by the entropy of each token’s training-set tag distribution.The procedure calculates the tag distribution for a token and uses entropy to quantify prediction difficulty.
  • Named entity recognition: Higher tag-distribution entropy corresponds to higher quantified data uncertainty for NER tokens.Tokens with more evenly distributed possible tags receive higher entropy and higher estimated uncertainty.
  • Named entity recognition: Higher average data uncertainty is observed for more difficult NER tags, indicating a strong correlation with prediction confidence.

Conclusion

The paper evaluates uncertainty quantification across three NLP tasks using convolutional and recurrent neural networks. Modeling both uncertainties improves performance, while higher data uncertainty identifies more difficult predictions.

  • Experiments cover sentiment analysis, named entity recognition, and language modeling with convolutional and recurrent neural network models.
  • Quantifying both model and data uncertainties improves model performance across all three NLP tasks.
  • Higher data uncertainty is estimated for more difficult predictions in both Yelp 2013 and CoNLL 2003.The paper identifies fully utilizing estimated uncertainties as a future research direction.
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