Papers with constituent parsing
Two Local Models for Neural Constituent Parsing (C18-1)
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| Challenge: | Non-local features have been shown crucial for statistical parsing, but local models can give highly competitive accuracies thanks to the power of dense neural input representations. |
| Approach: | They propose to use local neural models for constituent parsing to capture dependencies between sub output structures and to exploit non-local features. |
| Outcome: | The proposed model achieves labeled bracketing F1 scores of 92.4% on PTB and 87.3% on CTB 5.1. |
Implementation of a Chomsky-Schützenberger n-best parser for weighted multiple context-free grammars (N19-1)
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| Challenge: | Constituent parsing has been studied extensively in the last decades. |
| Approach: | They propose to decompose a language into a regular language, a homomorphism, and a bracket language to divide the parsing problem into simpler subproblems. |
| Outcome: | The proposed approach is comparable to state-of-the-art grammar-based parsers. |
An Empirical Study of Building a Strong Baseline for Constituency Parsing (P18-2)
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| Challenge: | Sequence-to-sequence models have been used for natural language generation tasks such as machine translation and summarization. |
| Approach: | They propose to build a strong baseline based on general purpose sequence-to-sequence models for constituency parsing. |
| Outcome: | The proposed model outperforms existing models in natural language generation tasks without any explicit task-specific knowledge or architecture of constituent parsing. |
Dynamic Oracles for Top-Down and In-Order Shift-Reduce Constituent Parsing (D18-1)
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| Challenge: | Top-down and in-order shift-reduce constituent parsers are the most accurate known shift-reducing algorithms for constituent paring. |
| Approach: | They propose to use dynamic oracles to train two of the most accurate shift-reduce algorithms for constituent parsing. |
| Outcome: | The proposed top-down and in-order shift-reduce parsers improve on the WSJ benchmark. |
Constituent Parsing as Sequence Labeling (D18-1)
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| Challenge: | Constituent parsing is a core problem in NLP where the goal is to obtain the syntactic structure of sentences expressed as a phrase structure tree. |
| Approach: | They propose a method to reduce constituent parsing to sequence labeling by using a tree with unary branches. |
| Outcome: | The proposed method outperforms the Vinyals et al. (2015) sequence-to-sequence parser by 90% on the PTB and CTB treebanks. |
Improving Neural Machine Translation with Neural Syntactic Distance (N19-1)
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| Challenge: | Neural syntactic distance (NSD) is used to represent constituent trees using a sequence whose length is identical to the number of words in the sentence. |
| Approach: | They propose five strategies to improve NMT with explicit use of syntactic information . et al., 2014) propose a set of five strategies that incorporate syntastic information into the encoder and/or decoder of the baseline model. |
| Outcome: | The proposed strategies improve translation performance of the baseline model (+2.1 (En–Ja), +1.3 (Ja–En), +1.2 (En-Ch), and +1.0 (Ch–En) BLEU. |
Discontinuous Constituent Parsing as Sequence Labeling (2020.emnlp-main)
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| Challenge: | Existing approaches to discontinuous parsing are complex and low-level. |
| Approach: | They propose to encode discontinuities as nearly ordered permutations of the input sequence. |
| Outcome: | The proposed model is fast and accurate under the right representation. |
Head-Driven Phrase Structure Grammar Parsing on Penn Treebank (P19-1)
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| Challenge: | Head-driven phrase structure grammars have a uniform formalism representing rich contextual syntactic and even semantic meanings. |
| Approach: | They propose to integrate constituent and dependency formal representations into head-driven phrase structure. |
| Outcome: | The proposed parser achieves state-of-the-art performance on Penn Treebank and Chinese Penn TreeBank. |
Better, Faster, Stronger Sequence Tagging Constituent Parsers (N19-1)
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| Challenge: | Existing efforts to speed up constituent parsing have focused on chart-based or shift-reduce parsers. |
| Approach: | They propose to use auxiliary losses and sentence-level fine-tuning to mitigate greedy decoding issues. |
| Outcome: | The proposed model surpasses the performance of sequence tagging constituent parsers on the English and Chinese Penn Treebank datasets and reduces their parsing time even further. |