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Training a model to provide natural language explanations (NLEs) for itspredictions usually requires the acquisition of task-specific NLEs, which istime- and resource-consuming. A potential solution is the few-shotout-of-domain transfer of NLEs from a parent task with many NLEs to a childtask. In this work, we examine the setup in which the child task has few NLEsbut abundant labels. We establish four few-shot transfer learning methods thatcover the possible fine-tuning combinations of the labels and NLEs for theparent and child tasks. We transfer explainability from a large naturallanguage inference dataset (e-SNLI) separately to two child tasks: (1) hardcases of pronoun resolution, where we introduce the small-e-WinoGrande datasetof NLEs on top of the WinoGrande dataset, and (2)~commonsense validation(ComVE). Our results demonstrate that the parent task helps with NLE generationand we establish the best methods for this setup.

Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant Setup