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.

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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.
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Challenge: Discontinuous constituency trees are derivations of Linear Context-Free Rewriting Systems (LCFRS), which makes them much harder to parse.
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Neural Combinatory Constituency Parsing (2021.findings-acl)

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Challenge: Existing approaches to constituency parsing are based on symbolic engineering, but they are simplified by their adaptive distributed representation.
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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 .
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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.
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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.
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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.
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Reducing Discontinuous to Continuous Parsing with Pointer Network Reordering (2021.emnlp-main)

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Challenge: Existing discontinuous constituent parsers are slow and lack accuracy and speed . however, discontinuous parsing can be solved by any off-the-shelf continuous parser .
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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.
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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.
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