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- W2950470622 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." @default.
- W2950470622 created "2019-06-27" @default.
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- W2950470622 date "2019-01-01" @default.
- W2950470622 modified "2023-09-24" @default.
- W2950470622 title "Misleading Failures of Partial-input Baselines" @default.
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- W2950470622 doi "https://doi.org/10.18653/v1/p19-1554" @default.
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