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The New England Journal of Statistics in Data Science


Conformal Prediction for Text Infilling and Part-of-Speech Prediction
Volume 1, Issue 1 (2023), pp. 69–83
Pub. online: 5 October 2022      Type: Machine Learning And Data Mining      Open accessOpen Access
Accepted
18 August 2022
Published
5 October 2022

Abstract

Modern machine learning algorithms are capable of providing remarkably accurate point-predictions; however, questions remain about their statistical reliability. Unlike conventional machine learning methods, conformal prediction algorithms return confidence sets (i.e., set-valued predictions) that correspond to a given significance level. Moreover, these confidence sets are valid in the sense that they guarantee finite sample control over type 1 error probabilities, allowing the practitioner to choose an acceptable error rate. In our paper, we propose inductive conformal prediction (ICP) algorithms for the tasks of text infilling and part-of-speech (POS) prediction for natural language data. We construct new ICP-enhanced algorithms for POS tagging based on BERT (bidirectional encoder representations from transformers) and BiLSTM (bidirectional long short-term memory) models. For text infilling, we design a new ICP-enhanced BERT algorithm. We analyze the performance of the algorithms in simulations using the Brown Corpus, which contains over 57,000 sentences. Our results demonstrate that the ICP algorithms are able to produce valid set-valued predictions that are small enough to be applicable in real-world applications. We also provide a real data example for how our proposed set-valued predictions can improve machine generated audio transcriptions.

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© 2023 New England Statistical Society
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Keywords
BERT BiLSTM Natural language processing Set-valued prediction Uncertainty quantification

Funding
Research reported in this publication was supported by the National Science Foundationa and the National Security Agency under Award Numbers 2051010 and H98230-21-1-0014, respectively.

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