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Natural language explanations promise to offer intuitively understandableexplanations of a neural network's decision process in complex vision-languagetasks, as pursued in recent VL-NLE models. While current models offerimpressive performance on task accuracy and explanation plausibility, theysuffer from a range of issues: Some models feature a modular design where theexplanation generation module is poorly integrated with a separate module fortask-answer prediction, employ backbone models trained on limited sets oftasks, or incorporate ad hoc solutions to increase performance on singledatasets. We propose to evade these limitations by applying recent advances inlarge-scale multi-task pretraining of generative Transformer models to theproblem of VL-NLE tasks. Our approach outperforms recent models by a largemargin, with human annotators preferring the generated explanations over theground truth in two out of three evaluated datasets. As a novel challenge inVL-NLE research, we propose the problem of multi-task VL-NLE and show thatjointly training on multiple tasks can increase the explanation quality. Wediscuss the ethical implications of high-quality NLE generation and otherissues in recent VL-NLE research.

Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language Explanations