Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oracles
James Cross, Liang Huang · 2016
Parsing accuracy using efficient greedy transition systems has improved dramatically in recent years thanks to neural networks.Despite striking results in dependency parsing, however, neural models have not surpassed stateof-the-art approaches in constituency parsing.To remedy this, we introduce a new shiftreduce system whose stack contains merely sentence spans, represented by a bare minimum of LSTM features.We also design the first provably optimal dynamic oracle for constituency parsing, which runs in amortized O(1) time, compared to O(n 3 ) oracles for standard dependency parsing.Training with this oracle, we achieve the best F 1 scores on both English and French of any parser that does not use reranking or external data.