AI/ML

Misleading Failures of Partial-input Baselines. (arXiv:1905.05778v1 [cs.LG])



Recent work establishes dataset difficulty and removes annotation artifacts
via partial-input baselines (e.g., hypothesis-only or image-only models). While
the success of a partial-input baseline indicates a dataset is cheatable, our
work cautions the converse is not necessarily true. Using artificial datasets,
we illustrate how the failure of a partial-input baseline might shadow more
trivial patterns that are only visible in the full input. We also identify such
artifacts in real natural language inference datasets. Our work provides an
alternative view on the use of partial-input baselines in future dataset
creation.

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