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In order to reveal the rationale behind model predictions, many works haveexploited providing explanations in various forms. Recently, to furtherguarantee readability, more and more works turn to generate sentence-levelhuman language explanations. However, current works pursuing sentence-levelexplanations rely heavily on annotated training data, which limits thedevelopment of interpretability to only a few tasks. As far as we know, thispaper is the first to explore this problem smoothly from weak-supervisedlearning to unsupervised learning. Besides, we also notice the high latency ofautoregressive sentence-level explanation generation, which leads toasynchronous interpretability after prediction. Therefore, we propose anon-autoregressive interpretable model to facilitate parallel explanationgeneration and simultaneous prediction. Through extensive experiments onNatural Language Inference task and Spouse Prediction task, we find that usersare able to train classifiers with comparable performance $10-15\times$ fasterwith parallel explanation generation using only a few or no annotated trainingdata.

Parallel Sentence-Level Explanation Generation for Real-World Low-Resource Scenarios