Structural Embedding of Syntactic Trees for Machine Comprehension

Rui LiuJunjie HuWei WeiZi YangEric Nyberg

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Deep neural networks for machine comprehension typically utilizes only word or character embeddings without explicitly taking advantage of structured linguistic information such as constituency trees and dependency trees. In this paper, we propose structural embedding of syntactic trees (SEST), an algorithm framework to utilize structured information and encode them into vector representations that can boost the performance of algorithms for the machine comprehension... (read more)

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