Learning Recurrent Span Representations for Extractive Question Answering

@article{Lee2016LearningRS,
  title={Learning Recurrent Span Representations for Extractive Question Answering},
  author={Kenton Lee and Tom Kwiatkowski and Ankur P. Parikh and Dipanjan Das},
  journal={CoRR},
  year={2016},
  volume={abs/1611.01436}
}
The reading comprehension task, that asks questions about a given evidence document, is a central problem in natural language understanding. Recent formulations of this task have typically focused on answer selection from a set of candidates pre-defined manually or through the use of an external NLP pipeline. However, Rajpurkar et al. (2016) recently released the SQUAD dataset in which the answers can be arbitrary strings from the supplied text. In this paper, we focus on this answer extraction… CONTINUE READING
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