Challenge: Discontinuous constituency trees are derivations of Linear Context-Free Rewriting Systems (LCFRS), which makes them much harder to parse.
Approach: They propose a transition system that uses a set of parsing items with constant-time random access instead of storing subtrees in a stack .
Outcome: The proposed system constructs a discontinuous constituency tree in 4n–2 transitions for a sentence of length n.

Similar Papers

Reorder and then Parse, Fast and Accurate Discontinuous Constituency Parsing (2022.emnlp-main)

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Challenge: Discontinuous constituency parsing is still being developed for its efficiency and accuracy are far behind its continuous counterparts.
Approach: They propose to transform a discontinuous constituent tree into a pseudo-continuous one by reordering words in the sentence.
Outcome: The proposed method can transform a discontinuous constituent tree into a pseudo-continuous one by parsing and performing actions on three classical discontinuous constituency treebanks.
Discontinuous Combinatory Constituency Parsing (2023.tacl-1)

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Challenge: Discontinuous parsing is more challenging than continuous parsers because children can group with syntactic cousins in the sentence rather than its two adjacent neighbors.
Approach: They extend a pair of combinator-based constituency parsers into a discontinuous pair . they use a swap action and biaffine attention to iteratively compose constituent vectors from word embeddings without any grammar constraints.
Outcome: The proposed parsers achieve state-of-the-art discontinuous accuracy with a significant speed advantage over continuous parsing.
Span-based discontinuous constituency parsing: a family of exact chart-based algorithms with time complexities from O(nˆ6) down to O(nˆ3) (2020.emnlp-main)

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Challenge: a novel chart-based parser for discontinuous constituency trees is proposed for span-based span parsing . it can process discontinuous constituent trees of block degree two, including ill-nested structures .
Approach: They propose a chart-based algorithm for span-based parsing of discontinuous constituency trees . they build variants with smaller search spaces and time complexities ranging from O(n6) down to O(N3) .
Outcome: The proposed algorithm can process 98% of constituents in linguistic treebanks while having the same complexity as continuous constituency parsers.
Don’t Parse, Choose Spans! Continuous and Discontinuous Constituency Parsing via Autoregressive Span Selection (2023.acl-long)

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Challenge: Constituency parsing is a fundamental task in natural language processing, having many applications in downstream tasks such as language modeling.
Approach: They propose a simple and unified approach for both continuous and discontinuous constituency parsing via autoregressive span selection.
Outcome: The proposed model can predict all possible continuous and discontinuous constituency trees without sacrificing data coverage and without expensive chart-based parsing algorithms.
BERT-Proof Syntactic Structures: Investigating Errors in Discontinuous Constituency Parsing (2021.findings-acl)

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Challenge: Recent results show that pretrained language models can be used for many tasks with high accuracy and high performance.
Approach: They propose two methods for automatically analysing discontinuous parsers' errors.
Outcome: The proposed methods characterize errors of a state-of-the-art transition-based discontinuous parser and provide an overview of the contribution of BERT to this task.
Training a Swedish Constituency Parser on Six Incompatible Treebanks (2020.lrec-1)

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Challenge: Syntactic parsing is a widely used intermediate step in several natural language processing tasks.
Approach: They propose to use a function-tagged constituent treebank for Swedish which includes discontinuous constituents to improve the accuracy.
Outcome: The proposed parser can be trained on additional treebanks that use other annotation models.
Parsing Headed Constituencies (2024.lrec-main)

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Challenge: Using constituency and dependency trees, syntactic representations are preferred for tasks such as nominal phrase extraction and identification of terminology.
Approach: They propose a parsing technique that generates headed constituency trees which combine information typically contained in constituency and dependency trees.
Outcome: The proposed method generates headed constituency trees with discontinuities and can generate constituency tree with discontinuity.
Linear-time Constituency Parsing with RNNs and Dynamic Programming (P18-2)

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Challenge: Existing span-based constituency parsers are too slow for longer sentences and for applications beyond sentence boundaries.
Approach: They propose a linear-time constituency parser with RNNs and dynamic programming using graph-structured stack and beam search.
Outcome: The proposed parser is faster for long sentences and faster for discourse parsing.
Straight to the Tree: Constituency Parsing with Neural Syntactic Distance (P18-1)

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Challenge: Compared to traditional shift-reduce parsing schemes, our approach is free from the potentially disastrous compounding error.
Approach: They propose a model that predicts a scalar for each split position in a sentence and then determines the topology of grammar tree based on syntactic distances.
Outcome: The proposed model achieves the state-of-the-art single model F1 score of 92.1 on PTB and 86.4 on CTB dataset, surpassing the previous single model results by a large margin.
A Dynamic Oracle for Linear-Time 2-Planar Dependency Parsing (N18-2)

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Challenge: Existing dynamic oracles for greedy parsers can handle non-projective syntax, but none are available for these types of training.
Approach: They propose an efficient dynamic oracle for training the 2-Planar transition-based parser with over 99% coverage on non-projective syntactic corpora.
Outcome: The proposed model outperforms the static training strategy in the vast majority of languages tested and scored better on most datasets than the arc-hybrid parser enhanced with the Swap transition.

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