  Natural Language Inference (NLI) models are known to learn from biases and
artefacts within their training data, impacting how well they generalise to
other unseen datasets. Existing de-biasing approaches focus on preventing the
models from learning these biases, which can result in restrictive models and
lower performance. We instead investigate teaching the model how a human would
approach the NLI task, in order to learn features that will generalise better
to previously unseen examples. Using natural language explanations, we
supervise the model's attention weights to encourage more attention to be paid
to the words present in the explanations, significantly improving model
performance. Our experiments show that the in-distribution improvements of this
method are also accompanied by out-of-distribution improvements, with the
supervised models learning from features that generalise better to other NLI
datasets. Analysis of the model indicates that human explanations encourage
increased attention on the important words, with more attention paid to words
in the premise and less attention paid to punctuation and stop-words.
