Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets

TACL 2017 Rotem DrorGili BaumerMarina BogomolovRoi Reichart

With the ever-growing amounts of textual data from a large variety of languages, domains, and genres, it has become standard to evaluate NLP algorithms on multiple datasets in order to ensure consistent performance across heterogeneous setups. However, such multiple comparisons pose significant challenges to traditional statistical analysis methods in NLP and can lead to erroneous conclusions... (read more)

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