# Temporal and Aspectual Entailment

## TL;DR

This paper introduces a new dataset to evaluate NLP models' understanding of tense and aspect in temporal inferences, revealing that models encode some morphosyntactic info but struggle with semantic reasoning involving these concepts.

## Contribution

The paper presents a novel entailment dataset focused on temporal and aspectual inference and analyzes how well NLP models capture these semantic properties.

## Key findings

- Models encode morphosyntactic information about tense and aspect.
- Models fail to perform inference requiring reasoning with tense and aspect.
- The dataset enables evaluation of semantic understanding in NLP models.

## Abstract

Inferences regarding "Jane's arrival in London" from predications such as "Jane is going to London" or "Jane has gone to London" depend on tense and aspect of the predications. Tense determines the temporal location of the predication in the past, present or future of the time of utterance. The aspectual auxiliaries on the other hand specify the internal constituency of the event, i.e. whether the event of "going to London" is completed and whether its consequences hold at that time or not. While tense and aspect are among the most important factors for determining natural language inference, there has been very little work to show whether modern NLP models capture these semantic concepts. In this paper we propose a novel entailment dataset and analyse the ability of a range of recently proposed NLP models to perform inference on temporal predications. We show that the models encode a substantial amount of morphosyntactic information relating to tense and aspect, but fail to model inferences that require reasoning with these semantic properties.