  Natural Language Inference (NLI) models are known to learn from biases and
artefacts within their training data, impacting how well the models generalise
to other unseen datasets. While previous de-biasing approaches focus on
preventing models learning from these biases, we instead provide models with
information about how a human would approach the task, with the aim of
encouraging the model to learn features that will generalise better to
out-of-domain datasets. Using natural language explanations, we supervise a
model's attention weights to encourage more attention to be paid to the words
present in these explanations. For the first time, we show that training with
human generated explanations can simultaneously improve performance both
in-distribution and out-of-distribution for NLI, whereas most related work on
robustness involves a trade-off between the two. Training with the human
explanations encourages models to attend more broadly across the sentences,
paying more attention to words in the premise and less attention to stop-words
and punctuation. The supervised models attend to words humans believe are
important, creating more robust and better performing NLI models.
