AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

Large neural-based Pre-trained Language Models (PLM) have recently gained much
attention due to their noteworthy performance in many downstream Information Retrieval …

Question Answering (QA) has been successfully applied in scenarios of human-computer
interaction such as chatbots and search engines. However, for the specific biomedical …

Pre-trained language models (PLMs), such as BERT and GPT, have revolutionized the field
of NLP, not only in the general domain but also in the biomedical domain. Most prior efforts …

Biomedical text summarization (BTS) is proving to be an emerging area of work and
research with the need for sustainable healthcare applications such as evidence-based …

Knowledge graph integration typically suffers from the widely existing dangling entities that
cannot find alignment cross knowledge graphs (KGs). The dangling entity set is unavailable …

With the outbreak of COVID-19 that has prompted an increased focus on self-care, more and
more people hope to obtain disease knowledge from the Internet. In response to this …

Medical question summarization is an important but difficult task, where the input is often
complex and erroneous while annotated data is expensive to acquire. We report our …

Distantly-supervised named entity recognition (NER) locates and classifies entities using
only knowledge bases and unlabeled corpus to mitigate the reliance on human-annotated …

Recent advances have witnessed a trending application of transfer learning in a broad
spectrum of natural language processing (NLP) tasks, including question answering (QA) …