Graph Attention Networks (GATs) are one of the most popular GNN architectures and are
considered as the state-of-the-art architecture for representation learning with graphs. In …

This paper is a condensed report on Touché: the first shared task on argument retrieval that
was held at CLEF 2020. With the goal to create a collaborative platform for research in …

This paper is a condensed report on the second year of the Touché shared task on
argument retrieval held at CLEF 2021. With the goal to provide a collaborative platform for …

Graph neural networks (GNNs) aim to learn well-trained representations in a lower-
dimension space for downstream tasks while preserving the topological structures. In recent …

This paper is a condensed report on the third year of the Touché lab on argument retrieval
held at CLEF 2022. With the goal to foster and support the development of technologies for …

This paper aims to enhance the few-shot relation classification especially for sentences that
jointly describe multiple relations. Due to the fact that some relations usually keep high co …

In this paper, we analyze comparative questions and answers. At least 3%~ of the questions
submitted to search engines are comparative; ranging from simple facts like" Did Messi or …

This paper presents a physics-guided machine learning approach that incorporates partial
differential equations (PDEs) in a graph neural network model to improve the prediction of …

This paper proposes to study a fine-grained semantic novelty detection task, which can be
illustrated with the following example. It is normal that a person walks a dog in the park, but if …

We study the task of span-level emotion cause analysis (SECA), which is focused on
identifying the specific emotion cause span (s) triggering a certain emotion in the text …