Neural networks have recently achieved human-level performance on various challenging natural language processing (NLP) tasks, but it is notoriously difficult to understand why a neural network produced a particular prediction. In this paper, we leverage the text-to-text framework proposed by Raffel et al.(2019) to train language models to output a natural text explanation alongside their prediction. Crucially, this requires no modifications to the loss function or training and decoding procedures -- we simply train the model to output the explanation after generating the (natural text) prediction. We show that this approach not only obtains state-of-the-art results on explainability benchmarks, but also permits learning from a limited set of labeled explanations and transferring rationalization abilities across datasets. To facilitate reproducibility and future work, we release our code use to train the models.
@article{arxiv.2004.14546,
title = {WT5?! Training Text-to-Text Models to Explain their Predictions},
author = {Sharan Narang and Colin Raffel and Katherine Lee and Adam Roberts and Noah Fiedel and Karishma Malkan},
journal= {arXiv preprint arXiv:2004.14546},
year = {2020}
}