In-Order Transition-based Constituent Parsing

By Jiangming Liu. 2017

79 citations recorded by the source corpus.

Both bottom-up and top-down strategies have been used for neural transition-based constituent parsing. The parsing strategies differ in terms of the order in which they recognize productions in the derivation tree, where bottom-up strategies and top-down strategies take post-order and pre-order traversal over trees, respectively. Bottom-up parsers benefit from rich features from readily built partial parses, but lack lookahead guidance in the parsing process; top-down parsers benefit from non-local guidance for local decisions, but rely on a strong encoder over the input to predict a constituent hierarchy before its construction. To mitigate both issues, we propose a novel parsing system based on in-order traversal over syntactic trees, designing a set of transition actions to find a compromise between bottom-up constituent information and top-down lookahead information. Based on stack-LSTM, our psycholinguistically motivated constituent parsing system achieves 91.8 F1 on the WSJ benchmark. Furthermore, the system achieves 93.6 F1 with supervised reranking and 94.2 F1 with semi-supervised reranking, which are the best results on the WSJ benchmark.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Semi-Supervised Sequence Modeling with Cross-View Training (2018, methodology): “For dependency parsing, we omit results from Choe and Charniak (2016), Kuncoro et al. (2017), and Liu and Zhang (2017) because these train constituency parsers and convert the system outputs to dependency parses.”
Constituency Parsing with a Self-Attentive Encoder (2018, neutral): “8 Liu and Zhang (2017) – – 94.”
Cross-Domain Generalization of Neural Constituency Parsers (2019, methodology): “While neural models with rich modeling of syntactic structure have obtained strong performance on parsing (Dyer et al., 2016; Liu and Zhang, 2017) and a range of related tasks (Kuncoro et al., 2018; Hale et al., 2018), recent neural parsers obtain state-of-the-art F1 on benchmark datasets using…”
Head-Driven Phrase Structure Grammar Parsing on Penn Treebank (2019, unknown): “5 Liu and Zhang (2017b) 79.”
Natural Language Processing Advancements By Deep Learning: A Survey (2020, background): “Long Short Term Memory Network (LSTM) [31] is one of the most widely used classes of RNNs. LSTMs try to capture even long time dependencies between inputs from different time steps.”