Neural Combinatory Constituency Parsing
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Abstract (arXiv)
We propose two fast neural combinatory models for constituency parsing: binary and multi-branching. Our models decompose the bottom-up parsing process into 1) classification of tags, labels, and binary orientations or chunks and 2) vector composition based on the computed orientations or chunks. These models have theoretical sub-quadratic complexity and empirical linear complexity. The binary model achieves an F1 score of 92.54 on Penn Treebank, speeding at 1327.2 sents/sec. Both the models with XLNet provide near state-of-the-art accuracies for English. Syntactic branching tendency and headedness of a language are observed during the training and inference processes for Penn Treebank, Chinese Treebank, and Keyaki Treebank (Japanese).
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 24 of its 41 references that are in the map. First posted on arXiv 12 Jun 2021.
Builds on
24 of 41 references.
References · 24
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
- 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,898
- 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
- 2018Deep contextualized word representationscited by 2,285
- 2016Enriching Word Vectors with Subword Informationcited by 1,578
- 2018Constituency Parsing with a Self-Attentive Encodercited by 196
- 2018Multilingual Constituency Parsing with Self-Attention and Pre-Trainingcited by 117
- 2016Recurrent Neural Network Grammarscited by 109
- 2014Grammar as a Foreign Languagecited by 70
- 2017A Minimal Span-Based Neural Constituency Parsercited by 59
12 more
- 2020Fast and Accurate Neural CRF Constituency Parsingcited by 50
- 2019Head-Driven Phrase Structure Grammar Parsing on Penn Treebankcited by 43
- 2018Straight to the Tree: Constituency Parsing with Neural Syntactic Distancecited by 32
- 2018Constituent Parsing as Sequence Labelingcited by 26
- 2017In-Order Transition-based Constituent Parsingcited by 23
- 2020Strongly Incremental Constituency Parsing with Graph Neural Networkscited by 23
- 2016Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oraclescited by 20
- 2019Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inferencecited by 17
- 2016Shift-Reduce Constituent Parsing with Neural Lookahead Featurescited by 8
- 2017Encoder-Decoder Shift-Reduce Syntactic Parsingcited by 7
- 2020Efficient Constituency Parsing by Pointingcited by 7
- 2020A Span-based Linearization for Constituent Treescited by 6
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