  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.
