The past decade has witnessed an explosion of textual information in the biomedical field.
Biomedical texts provide a basis for healthcare delivery, knowledge discovery, and decision …

Information extraction (IE) is a challenging task, particularly when dealing with highly
heterogeneous data. State-of-the-art data mining technologies struggle to process …

Event extraction (EE) is a crucial research task for promptly apprehending event information
from massive textual data. With the rapid development of deep learning, EE based on deep …

The latest batch of research has equipped language models with the ability to attend over
relevant and factual information from non-parametric external sources, drawing a …

Long document summarization poses obstacles to current generative transformer-based
models because of the broad context to process and understand. Indeed, detecting long …

Pre-conceptual schemas are a straightforward way to represent knowledge using controlled
language regardless of context. Despite the benefits of using pre-conceptual schemas by …

In knowledge graph representation learning, link prediction is among the most popular and
influential tasks. Its surge in popularity has resulted in a panoply of orthogonal embedding …

The automatic synthesis of biomedical publications catalyzes a profound research interest
elicited by literature congestion. Current sequence-to-sequence models mainly rely on the …

In this paper, we address the problem of multi-modal retrieval of fashion products. State-of-
the-art (SOTA) works proposed in literature use vision-and-language transformers to assign …

Driven by deep learning breakthroughs, natural language generation (NLG) models have
been at the center of steady progress in the last few years, with a ubiquitous task influence …