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.

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Parser Training with Heterogeneous Treebanks (P18-2)

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Challenge: In the 2017 CoNLL Shared Task on Universal Dependency Parsing, 25 languages have more than one treebank . many teams did not take advantage of the multiple treebanks, however, and trained one model per treebank instead of one model for each language.
Approach: They propose a method to make the most of heterogeneous treebanks when training a monolingual parser.
Outcome: The proposed method improves on training with multiple treebanks for a single language.
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.
Treebank Embedding Vectors for Out-of-Domain Dependency Parsing (2020.acl-main)

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Challenge: a recent advance in monolingual dependency parsing is the idea of a treebank embedding vector . this allows the model to prefer training data from one treebank over another at test time .
Approach: They propose a method to predict a treebank vector for sentences that do not come from a particular treebank . they also explore what happens when they move away from predefined treebank embedding vectors .
Outcome: The proposed method can predict treebank vectors for sentences that do not come from a treebank used in training with sufficient accuracy for nine out of ten languages.
Cheating a Parser to Death: Data-driven Cross-Treebank Annotation Transfer (L18-1)

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Challenge: Using annotated corpus for linguistic purposes is no longer justified . hand-crafted syntactic resources such as grammars and lexicons can be used as sources of features to guide data driven systems.
Approach: They propose an efficient method for transferring annotations between two different treebanks of the same language.
Outcome: The proposed method is based on the Universal Dependency annotation scheme and was evaluated on the gold standard (94.75% of LAS, 99.40% UAS on the test set).
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.
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.
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.
Efficient Constituency Parsing by Pointing (2020.acl-main)

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Challenge: Constituency parsing is a core task in natural language processing (NLP) Existing methods for constituency paring are greedy transition-based or globally optimized.
Approach: They propose a constituency parsing model that casts the problem into a series of pointing tasks.
Outcome: The proposed model achieves 92.78 F1 without pre-trained models, which is faster than existing 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.
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.

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