In the NLP community, recent years have seen a surge of research activities that address
machines' ability to perform deep language understanding which goes beyond what is …

In the last decade, the problem of computational metaphor processing has garnered
immense attention from the domains of computational linguistics and cognition. A wide …

In the last few years, a number of NLP researchers have developed and participated in the
task of Recognizing Textual Entailment (RTE). This task encapsulates Natural Language …

As the number of AI systems keeps growing, it is fundamental to implement and develop
efficient control mechanisms to ensure the safe and proper functioning of machine learning …

Abstract Out-of-distribution (OOD) detection is a rapidly growing field due to new robustness
and security requirements driven by an increased number of AI-based systems. Existing …

Inference has been a central topic in artificial intelligence from the start, but while automatic
methods for formal deduction have advanced tremendously, comparatively little progress …

Detecting conflicting statements is a foundational text understanding task with applications
in information analysis. We propose an appropriate definition of contradiction for NLP tasks …

We propose a model of natural language inference which identifies valid inferences by their
lexical and syntactic features, without full semantic interpretation. We extend past work in …

We propose an approach to natural language inference based on a model of natural logic,
which identifies valid inferences by their lexical and syntactic features, without full semantic …

This paper presents the first use of a computational model of natural logic—a system of
logical inference which operates over natural language—for textual inference. Most current …