Temporality in General-Domain Entailment Graph Induction

Entailment Graphs based on open relation extraction run the risk of learning spurious entailments (e.g. win against ⊨ lose to) from antonymous predications that are observed with the same entities referring to different times. Previous research has demonstrated the potential of using temporality as a signal to avoid learning these entailments in the sports domain. We investigate whether this extends to the general news domain. Our method introduces a temporal window that is set dynamically for each eventuality using a temporally informed language model. We evaluate our models on a sports-specific dataset, and ANT – a novel general-domain dataset based on Word-Net antonym pairs. We find that whilst it may be useful to reinterpret the Distributional Inclusion Hypothesis to include time for the sports news domain, this does not apply to the general news domain.