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.

探究利用自然语言解释来监督模型的注意力权重，促使其更集中于解释中的关键词，从而提高模型性能，此方法的实验表明，这种改进可以适用于其它 NLI 数据集