Deep learning approaches exhibit promising performances on various text tasks. However,
they are still struggling on medical text classification since samples are often extremely …

Early time series classification is a variant of the time series classification task, in which a
label must be assigned to the incoming time series as quickly as possible without …

This study investigates the performance of the zero-shot method in classifying data using
three large language models, alongside two models with large input token sizes and the two …

Discourse relations play a pivotal role in establishing coherence within textual content,
uniting sentences and clauses into a cohesive narrative. The Penn Discourse Treebank …

Proper losses such as cross-entropy incentivize classifiers to produce class probabilities that
are well-calibrated on the training data. Due to the generalization gap, these classifiers tend …

Abstract The goal of Emotion Cause Pair Extraction (ECPE) is to explore the causes of
emotion changes and what causes a certain emotion. This paper proposes a three-step …

Confidence calibration in classification models, a technique to achieve accurate posterior
probability estimation for classification results, is crucial for assessing the likelihood of …

Meta-learning in the broader context concerns how an agent learns about their own
learning, allowing them to improve their learning process. Learning how to learn is not only …

Traditional Language Models like BERT are trained using raw text, split into separate
chunks using a tokenizer. These models suffer from 3 main challenges: a lack of context …