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.

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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.
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.
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.
Discontinuous Constituency and BERT: A Case Study of Dutch (2022.findings-acl)

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Challenge: a recent study has shown that large-scale language models fail to acquire aspects of linguistic theory due to their unanticipated performance.
Approach: They propose to use a context-sensitive formalism to derive grammars that capture verb nesting and verb raising in Dutch.
Outcome: The proposed model fails to acquire the dependencies examined in Dutch.
Challenges to Open-Domain Constituency Parsing (2022.findings-acl)

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Challenge: Existing findings on cross-domain constituency parsing are only made on a limited number of domains.
Approach: They manually annotate a high-quality constituency treebank containing five domains and analyze challenges to open-domain constituency parsing using a set of linguistic features.
Outcome: The proposed model significantly improves the performance of the proposed model on the domain-variant features.
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.
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.
What’s Going On in Neural Constituency Parsers? An Analysis (N18-1)

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Challenge: a number of differences have emerged between classical and modern constituency parsing approaches . structural components like grammars and feature-rich lexicons are becoming less central . recurrent neural networks have gained traction as a powerful and general purpose tool for representation .
Approach: They propose a model that implicitly learns to encode much of the same information as grammars and lexicons in the past.
Outcome: The proposed model outperforms state-of-the-art models under similar conditions.
Discontinuous Constituency Parsing with a Stack-Free Transition System and a Dynamic Oracle (N19-1)

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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.
An Empirical Comparison of Unsupervised Constituency Parsing Methods (2020.acl-main)

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Challenge: Existing methods for unsupervised constituency parsing are inconsistent due to data preprocessing, lexicalization, and evaluation metrics.
Approach: They propose to standardize experimental settings for better comparability between methods . they compare existing methods with those proposed by decade-old models .
Outcome: The proposed methods perform better than decade-old models on English and Japanese, respectively, compared with decade- old models.

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