Adversarial robustness has attracted much attention recently, and the mainstream solution is
adversarial training. However, the tradition of generating adversarial perturbations for each …

Taxonomies are hierarchical classifications widely exploited to facilitate downstream natural
language processing tasks. The taxonomy expansion task aims to incorporate emergent …

Grasping the commonsense properties of everyday concepts is an important prerequisite to
language understanding. While contextualised language models are reportedly capable of …

Automatic taxonomy completion aims to attach the emerging concept to an appropriate pair
of hypernym and hyponym in the existing taxonomy. Existing methods suffer from the …

Taxonomy completion, a self-supervised task, aims to add new concepts to an existing
taxonomy by attaching them to appropriate hypernym and hyponym pairs. Researchers …

The automatic extraction of hypernym knowledge from large language models like BERT is
an open problem, and it is unclear whether methods fail due to a lack of knowledge in the …

The sparse interactions between users and items have aggravated the difficulty of their
representations in recommender systems. Existing methods leverage tags to alleviate the …

Current course recommender systems primarily leverage learner-course interactions, course
content, learner preferences, and supplementary course details like instructor, institution …

In this paper, we explore the ability of the generative transformers to predict objects in IS-A
(hypo-hypernym) relations. We solve the task for both directions of the relations: we learn to …

Taxonomies, which organize domain concepts into hierarchical structures, are crucial for
building knowledge systems and downstream applications. As domain knowledge evolves …