Infectious disease outbreaks continue to pose a significant threat to hu...
Biomedical named entity recognition is one of the core tasks in biomedic...
Large language models (LLMs) have shown great abilities of solving vario...
The sequence-to-sequence (seq2seq) task aims at generating the target
se...
Event extraction is a complex information extraction task that involves
...
Fine-tuning a pre-trained model (such as BERT, ALBERT, RoBERTa, T5, GPT,...
Recent pre-trained language models have shown promising capabilities in
...
Fine-tuning pre-trained models has been ubiquitously proven to be effect...
Generating text with autoregressive language models (LMs) is of great
im...
Text generation is of great importance to many natural language processi...
Recent work indicated that pretrained language models (PLMs) such as BER...
Infusing factual knowledge into pre-trained models is fundamental for ma...
Recent developments in neural networks have led to the advance in
data-t...
Neural table-to-text generation models have achieved remarkable progress...
Injecting external domain-specific knowledge (e.g., UMLS) into pretraine...
Pretrained Masked Language Models (MLMs) have revolutionised NLP in rece...
Non-autoregressive generation (NAG) has recently attracted great attenti...
We study the learning of a matching model for dialogue response selectio...
Despite the widespread success of self-supervised learning via masked
la...
Whilst there has been growing progress in Entity Linking (EL) for genera...
This work studies the use of visual semantic representations to align
en...
In this paper we focus on unsupervised representation learning and propo...
We present a new challenging stance detection dataset, called
Will-They-...
The ability of a dialog system to express prespecified language style du...
Stylistic response generation is crucial for building an engaging dialog...
We present the Global Health Monitor, an online Web-based system for
det...
Identifying articles that relate to infectious diseases is a necessary s...
Variational Autoencoders (VAEs) are known to suffer from learning
uninfo...
We present a novel method for mapping unrestricted text to knowledge gra...
Word embedding techniques heavily rely on the abundance of training data...
Empirical methods in geoparsing have thus far lacked a standard evaluati...
Rare word representation has recently enjoyed a surge of interest, owing...
This paper addresses the problem of mapping natural language text to
kno...
Lexical ambiguity can impede NLP systems from accurate understanding of
...
We propose a methodology that adapts graph embedding techniques (DeepWal...
One major deficiency of most semantic representation techniques is that ...
Previous studies have shown that health reports in social media, such as...
The objective of change-point detection is to discover abrupt property
c...