Natural Language Comprehension with the EpiReader Natural Language Comprehension with the EpiReader
Paper summary TLDR; The authors prorpose the "EpiReader" model for Question Answering / Machine Comprehension. The model consists of two modules: An Extractor that selects answer candidates (single words) using a Pointer network, and a Reasoner that rank these candidates by estimating textual entailment. The model is trained end-to-end and works on cloze-style questions. The authors evaluate the model on CBT and CNN datasets where they beat Attention Sum Reader and MemNN architectures. #### Notes - In most architectures, the correct answer is among the top5 candidates 95% of the time. - Soft Attention is a problem in many architectures. Need a way to do hard attention.
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Natural Language Comprehension with the EpiReader
Adam Trischler and Zheng Ye and Xingdi Yuan and Kaheer Suleman
arXiv e-Print archive - 2016 via arXiv
Keywords: cs.CL

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