mlsanityData to 7 Oct 2026

In-Order Chart-Based Constituent Parsing

arXiv 2102.04065 ↗First posted 8 Feb 2021cs.CLcited by 1

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Abstract (arXiv)

We propose a novel in-order chart-based model for constituent parsing. Compared with previous CKY-style and top-down models, our model gains advantages from in-order traversal of a tree (rich features, lookahead information and high efficiency) and makes a better use of structural knowledge by encoding the history of decisions. Experiments on the Penn Treebank show that our model outperforms previous chart-based models and achieves competitive performance compared with other discriminative single models.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 27 references that are in the map. First posted on arXiv 8 Feb 2021.

Builds on

19 of 27 references.

References · 19

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2018Constituency Parsing with a Self-Attentive Encodercited by 196
  4. 2018Multilingual Constituency Parsing with Self-Attention and Pre-Trainingcited by 117
  5. 2016Recurrent Neural Network Grammarscited by 109
  6. 2014Grammar as a Foreign Languagecited by 70
  7. 2017A Minimal Span-Based Neural Constituency Parsercited by 59
  8. 2015Transition-Based Dependency Parsing with Stack Long Short-Term Memorycited by 45
  9. 2018Straight to the Tree: Constituency Parsing with Neural Syntactic Distancecited by 32
  10. 2018Constituent Parsing as Sequence Labelingcited by 26
  11. 2017Effective Inference for Generative Neural Parsingcited by 24
  12. 2017In-Order Transition-based Constituent Parsingcited by 23
7 more
  1. 2018What's Going On in Neural Constituency Parsers? An Analysiscited by 22
  2. 2016Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oraclescited by 20
  3. 2016What Do Recurrent Neural Network Grammars Learn About Syntax?cited by 15
  4. 2016Shift-Reduce Constituent Parsing with Neural Lookahead Featurescited by 8
  5. 2018Two Local Models for Neural Constituent Parsingcited by 6
  6. 2018Linear-Time Constituency Parsing with RNNs and Dynamic Programmingcited by 2
  7. 2018Dynamic Oracles for Top-Down and In-Order Shift-Reduce Constituent Parsingcited by 2

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Citing papers per year: 2021: 1.

1 paper.

  1. 2021Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networkscited by 21