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We propose solutions to enhance the Inside-Outside Recursive Neural Network (IORNN) reranker of Le and Zuidema (2014). Replacing the original softmax function with a hierarchical softmax using a binary tree constructed by combining output of the Brown clustering algorithm and frequency-based Huffman codes, we significantly reduce the reranker’s computational complexity. In addition, enriching contexts used in the reranker by adding subtrees rooted at (ancestors’) cousin nodes, the accuracy is increased.

Enhancing the Inside-Outside Recursive Neural Network Reranker for Dependency Parsing