Automatic Question Answering (QA) has been successfully applied in various domains such
as search engines and chatbots. Biomedical QA (BQA), as an emerging QA task, enables …

Abstract Natural Language Processing (NLP) is a branch of artificial intelligence that
involves the design and implementation of systems and algorithms able to interact through …

Open domain question answering (OpenQA) tasks have been recently attracting more and
more attention from the natural language processing (NLP) community. In this work, we …

The BioASQ question answering (QA) benchmark dataset contains questions in English,
along with golden standard (reference) answers and related material. The dataset has been …

This is an overview of the eleventh edition of the BioASQ challenge in the context of the
Conference and Labs of the Evaluation Forum (CLEF) 2023. BioASQ is a series of …

Infusing factual knowledge into pre-trained models is fundamental for many knowledge-
intensive tasks. In this paper, we proposed Mixture-of-Partitions (MoP), an infusion approach …

Question answering (QA) models often rely on large-scale training datasets, which
necessitates the development of a data generation framework to reduce the cost of manual …

The retrieval model is an indispensable component for real-world knowledge-intensive
tasks, eg, open-domain question answering (ODQA). As separate retrieval skills are …

Advancing the state-of-the-art in large-scale biomedical semantic indexing and question
answering is the main focus of the BioASQ challenge. BioASQ organizes respective tasks …

Developing technology to assist medical experts in their everyday decision-making is
currently a hot topic in the field of Artificial Intelligence (AI). This is specially true within the …