Supervising Model Attention with Human Explanations for Robust Natural Language Inference

Understand

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.