  Natural Language Inference (NLI) is the task of determining the semantic
relationship between a premise and a hypothesis. In this paper, we focus on the
{\em generation} of hypotheses from premises in a multimodal setting, to
generate a sentence (hypothesis) given an image and/or its description
(premise) as the input. The main goals of this paper are (a) to investigate
whether it is reasonable to frame NLI as a generation task; and (b) to consider
the degree to which grounding textual premises in visual information is
beneficial to generation. We compare different neural architectures, showing
through automatic and human evaluation that entailments can indeed be generated
successfully. We also show that multimodal models outperform unimodal models in
this task, albeit marginally.
