Temporal and Aspectual Entailment
This addresses a gap in NLP for understanding temporal and aspectual entailment, but it is incremental as it focuses on dataset creation and analysis rather than new methods.
The paper tackled the problem of whether modern NLP models capture tense and aspect for natural language inference by proposing a novel entailment dataset and analyzing various models. The result showed that while models encode morphosyntactic information, they fail at reasoning with semantic properties.
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.