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 conformal prediction-enhanced bidirectional
encoder representations from transformers (BERT) and bidirectional long
short-term memory (BiLSTM) algorithms for POS tagging and a new conformal
prediction-enhanced BERT algorithm for text infilling. 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.

本研究提出基于收缩预测算法的文本填充和词性预测算法，并将其应用于自然语言数据处理中。通过在波士顿语料库中进行仿真实验，结论表明该算法能够生成有效的置信区间，并在机器语音转录中有改进应用。