Language models demonstrate both quantitative improvement and new qualitative
capabilities with increasing scale. Despite their potentially transformative impact, these new …

Recognizing Textual Entailment (RTE) was proposed as a unified evaluation framework to
compare semantic understanding of different NLP systems. In this survey paper, we provide …

Temporal commonsense reasoning refers to the ability to understand the typical temporal
context of phrases, actions, and events, and use it to reason over problems requiring such …

Modelling and understanding time remains a challenge in contemporary video
understanding models. With language emerging as a key driver towards powerful …

Everyday conversations require understanding everyday events, which in turn, requires
understanding temporal commonsense concepts interwoven with those events. Despite …

We propose TRACIE, a novel temporal reasoning dataset that evaluates the degree to which
systems understand implicit events--events that are not mentioned explicitly in natural …

Objective Seizure frequency and seizure freedom are among the most important outcome
measures for patients with epilepsy. In this study, we aimed to automatically extract this …

We introduce DocTime-a novel temporal dependency graph (TDG) parser that takes as input
a text document and produces a temporal dependency graph. It outperforms previous BERT …

Relation extraction has the potential for large-scale knowledge graph construction, but
current methods do not consider the qualifier attributes for each relation triplet, such as time …

We introduce a collection of recognizing textual entailment (RTE) datasets focused on
figurative language. We leverage five existing datasets annotated for a variety of figurative …