Data annotation is the labeling or tagging of raw data with relevant information, essential for
improving the efficacy of machine learning models. The process, however, is labor-intensive …

Common acquisition functions for active learning use either uncertainty or diversity
sampling, aiming to select difficult and diverse data points from the pool of unlabeled data …

As a main field of artificial intelligence, natural language processing (NLP) has achieved
remarkable success via deep neural networks. Plenty of NLP tasks have been addressed in …

In this work, we provide a survey of active learning (AL) for its applications in natural
language processing (NLP). In addition to a fine-grained categorization of query strategies …

Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within
the field of NLP, aimed at addressing limitations in existing frameworks while aligning with …

The proposed model for automatic clinical image caption generation combines the analysis
of radiological scans with structured patient information from the textual records. It uses two …

Abstract Models can fail in unpredictable ways during deployment due to task ambiguity,
when multiple behaviors are consistent with the provided training data. An example is an …

The quantification of aleatoric and epistemic uncertainty in terms of conditional entropy and
mutual information, respectively, has recently become quite common in machine learning …

In this work, we consider the problem of uncertainty estimation for Transformer-based
models. We investigate the applicability of uncertainty estimates based on dropout usage at …

Construction of human-curated annotated datasets for abstractive text summarization (ATS)
is very time-consuming and expensive because creating each instance requires a human …