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Parsing accuracy using efficient greedy transition systems has improveddramatically in recent years thanks to neural networks. Despite strikingresults in dependency parsing, however, neural models have not surpassedstate-of-the-art approaches in constituency parsing. To remedy this, weintroduce a new shift-reduce system whose stack contains merely sentence spans,represented by a bare minimum of LSTM features. We also design the firstprovably optimal dynamic oracle for constituency parsing, which runs inamortized O(1) time, compared to O(n^3) oracles for standard dependencyparsing. Training with this oracle, we achieve the best F1 scores on bothEnglish and French of any parser that does not use reranking or external data.

Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oracles