Abstract Multi-Label Text Classification (MLTC) is a fundamental and challenging task in
natural language processing. Previous studies mainly focus on learning text representation …

The development of deep learning techniques has allowed Neural Machine Translation
(NMT) models to become extremely powerful, given sufficient training data and training time …

Large language models (LLMs) that exhibit a remarkable ability by in-context learning (ICL)
with bilingual demonstrations have been recognized as a potential solution for machine …

Given the complicated label hierarchy, hierarchical text classification (HTC) has emerged as
a challenging subtask in the realm of multi-label text classification. Existing methods …

k-Nearest-Neighbor Machine Translation (kNN-MT) has been recently proposed as a non-
parametric solution for domain adaptation in neural machine translation (NMT). It aims to …

Semi-parametric models, which augment generation with retrieval, have led to impressive
results in language modeling and machine translation, due to their ability to retrieve fine …

Pre-trained language models (PLMs) have exhibited remarkable few-shot learning
capabilities when provided a few examples in a natural language prompt as demonstrations …