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How Self-Attention Improves Rare Class Performance in a Question-Answering Dialogue Agent
Contextualized language modeling using deep Transformer networks has been applied to a variety of natural language processing tasks with remarkable success. However, we find that these models are not a panacea for a questionanswering dialogue agent corpus task, which has hundreds of classes in a long-tailed frequency distribution, with only thousands of data points. Instead, we find substantial improvements in recall and accuracy on rare classes from a simple one-layer RNN with multi-headeddblp:conf/sigdial/StiffSF20 fatcat:c2bnxajhlfcrbdgohqqffycf6u