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

• URL: http://arxiv.org/abs/2004.14546v1
• Date: Thu, 30 Apr 2020 02:20:14 GMT
• Title: WT5?! Training Text-to-Text Models to Explain their Predictions
• Authors: Sharan Narang, Colin Raffel, Katherine Lee, Adam Roberts, Noah Fiedel, Karishma Malkan
• Abstract summary: 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. We show that this approach not only obtains state-of-the-art results on explainability, but also permits learning from a limited set of labeled explanations.
• Score: 38.59658315243017
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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