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arXiv · 1905.05778

Misleading Failures of Partial-input Baselines

Shi Feng·Eric Wallace·Jordan Boyd-Graber
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Abstract

Recent work establishes dataset difficulty and removes annotation artifacts via partial-input baselines (e.g., hypothesis-only models for SNLI or question-only models for VQA). When a partial-input baseline gets high accuracy, a dataset is cheatable. However, the converse is not necessarily true: the failure of a partial-input baseline does not mean a dataset is free of artifacts. To illustrate this, we first design artificial datasets which contain trivial patterns in the full input that are undetectable by any partial-input model. Next, we identify such artifacts in the SNLI dataset - a hypothesis-only model augmented with trivial patterns in the premise can solve 15% of the examples that are previously considered "hard". Our work provides a caveat for the use of partial-input baselines for dataset verification and creation.

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Shi Feng, Eric Wallace, Jordan Boyd-Graber. 2019-05-14. Misleading Failures of Partial-input Baselines. https://arxiv.org/abs/1905.05778

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