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Neural networks have recently achieved human-level performance on variouschallenging natural language processing (NLP) tasks, but it is notoriouslydifficult to understand why a neural network produced a particular prediction.In this paper, we leverage the text-to-text framework proposed by Raffel etal.(2019) to train language models to output a natural text explanationalongside their prediction. Crucially, this requires no modifications to theloss function or training and decoding procedures -- we simply train the modelto output the explanation after generating the (natural text) prediction. Weshow that this approach not only obtains state-of-the-art results onexplainability benchmarks, but also permits learning from a limited set oflabeled explanations and transferring rationalization abilities acrossdatasets. To facilitate reproducibility and future work, we release our codeuse to train the models.

WT5?! Training Text-to-Text Models to Explain their Predictions