Text classification tends to struggle when data is deficient or when it needs to adapt to
unseen classes. In such challenging scenarios, recent studies have used meta-learning to …

Word embedding is central to neural machine translation (NMT), which has attracted
intensive research interest in recent years. In NMT, the source embedding plays the role of …

Abstract In recent years, Recurrent Neural Network based Neural Machine Translation (RNN-
based NMT) equipped with an attention mechanism from the decoder to encoder, has …

Cross-attention is an important component of neural machine translation (NMT), which is
always realized by dot-product attention in previous methods. However, dot-product …

Pointer Generators have been the de facto standard for modern summarization systems.
However, this architecture faces two major drawbacks: Firstly, the pointer is limited to …

Clinical text and documents contain very rich information and knowledge in healthcare, and
their processing using state-of-the-art language technology becomes very important for …

Few-shot text classification aims to recognize new classes with only a few labeled text
instances. Previous studies mainly utilized text semantic features to model the instance-level …

The training objective of neural machine translation (NMT) is to minimize the loss between
the words in the translated sentences and those in the references. In NMT, there is a natural …

Ontology, as the foundational architecture for knowledge representation, necessitates
multilingualization to facilitate cross-lingual knowledge sharing, posing challenges that …

Generally, the decoder-only large language models (LLMs) are adapted to context-aware
neural machine translation (NMT) in a concatenating way, where LLMs take the …