![]() ![]() We also carry out a small user study to evaluate whether these methods are useful to NLP researchers in practice, with promising results. ![]() We show that this proposed training-feature attribution can be used to efficiently uncover artifacts in training data when a challenging validation set is available. We propose new hybrid approaches that combine saliency maps (which highlight "important" input features) with instance attribution methods (which retrieve training samples "influential" to a given prediction). ![]() In this paper we evaluate use of different attribution methods for aiding identification of training data artifacts. By the latter we mean spurious correlations between inputs and outputs that do not represent a generally held causal relationship between features and classes models that exploit such correlations may appear to perform a given task well, but fail on out of sample data. These are often collected automatically or via crowdsourcing, and may exhibit systematic biases or annotation artifacts. Training the deep neural networks that dominate NLP requires large datasets. ![]()
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