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Unifying Question Answering and Text Classification via Span Extraction

2019-04-19
Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, Richard Socher

Abstract

Even as pre-trained language encoders such as BERT are shared across many tasks, the output layers of question answering and text classification models are significantly different. Span decoders are frequently used for question answering and fixed-class, classification layers for text classification. We show that this distinction is not necessary, and that both can be unified as span extraction. A unified, span-extraction approach leads to superior or comparable performance in multi-task learning, low-data and supplementary supervised pretraining experiments on several text classification and question answering benchmarks.

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URL

http://arxiv.org/abs/1904.09286

PDF

http://arxiv.org/pdf/1904.09286


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