  We introduce a new inference task - Visual Entailment (VE) - which differs
from traditional Textual Entailment (TE) tasks whereby a premise is defined by
an image, rather than a natural language sentence as in TE tasks. A novel
dataset SNLI-VE (publicly available at https://github.com/necla-ml/SNLI-VE) is
proposed for VE tasks based on the Stanford Natural Language Inference corpus
and Flickr30k. We introduce a differentiable architecture called the
Explainable Visual Entailment model (EVE) to tackle the VE problem. EVE and
several other state-of-the-art visual question answering (VQA) based models are
evaluated on the SNLI-VE dataset, facilitating grounded language understanding
and providing insights on how modern VQA based models perform.
