Several studies have compared the in-distribution (ID) and out-of-distribution (OOD)
performance of models in computer vision and NLP. They report a frequent positive …

Logical reasoning is fundamental for humans yet presents a substantial challenge in the
domain of Artificial Intelligence. Initially, researchers used Knowledge Representation and …

Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness
remains elusive and constitutes a key issue that impedes large-scale adoption. Besides …

Abstract Out-of-distribution (OOD) generalization has attracted increasing research attention
in recent years, due to its promising experimental results in real-world applications …

Adversarial robustness, domain generalization and dataset biases are three active lines of
research contributing to out-of-distribution (OOD) evaluation on neural NLP models …

Generalizing to unseen image domains is a challenging problem primarily due to the lack of
diverse training data, inaccessible target data, and the large domain shift that may exist in …

While both extractive and generative readers have been successfully applied to the
Question Answering (QA) task, little attention has been paid toward the systematic …

Table Question Answering (TQA) is an important but under-explored task. Most of the
existing QA datasets are in unstructured text format and only few of them use tables as the …

The use of artificial intelligence (AI) in working environments with individuals, known as
Human-AI Collaboration (HAIC), has become essential in a variety of domains, boosting …

Abstract Unsupervised Domain Adaptation (UDA) is a learning technique that transfers
knowledge learned in the source domain from labelled training data to the target domain …