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The reading comprehension task, that asks questions about a given evidencedocument, is a central problem in natural language understanding. Recentformulations of this task have typically focused on answer selection from a setof candidates pre-defined manually or through the use of an external NLPpipeline. However, Rajpurkar et al. (2016) recently released the SQuAD datasetin which the answers can be arbitrary strings from the supplied text. In thispaper, we focus on this answer extraction task, presenting a novel modelarchitecture that efficiently builds fixed length representations of all spansin the evidence document with a recurrent network. We show that scoringexplicit span representations significantly improves performance over otherapproaches that factor the prediction into separate predictions about words orstart and end markers. Our approach improves upon the best published results ofWang & Jiang (2016) by 5% and decreases the error of Rajpurkar et al.'sbaseline by > 50%.

Learning Recurrent Span Representations for Extractive Question Answering