Challenge: scalable Universal Dependency (UD) treebank synthesis techniques are used to improve production-grade parsers.
Approach: They propose a data augmentation technique that uses synthetic treebanks to improve production-grade parsers.
Outcome: The proposed technique improves LAS performance on seven languages by up to two points on production models trained on original UD treebanks.

Similar Papers

Development of a Multilingual CCG Treebank via Universal Dependencies Conversion (2022.lrec-1)

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Challenge: Combinatory Categorial Grammar (CCG) is a lexicalized grammar formalism that can capture both syntactic and semantic information.
Approach: They propose an algorithm to convert UD treebanks to CCG treebank and propose future extensions.
Outcome: The proposed algorithm performs lexical, sentential, and syntactic rule coverage analysis, as well as CCG parsing experiments.
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.
Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection (2020.lrec-1)

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Challenge: Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages.
Approach: They describe version 2 of the universal guidelines and discuss major changes from UD v1 to UD 2 . they propose a morphological layer, a syntactic layer and a word segmentation layer .
Outcome: The proposed treebanks are available for 90 languages and have been updated to meet the needs of multilingual parsers and researchers.
Spoken Language Treebanks in Universal Dependencies: an Overview (2022.lrec-1)

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Challenge: spoken language treebanks have divergent annotation schemes limiting cross-resource explorations . many spoken language trees have no written form, but many of the world languages have no spoken form at all.
Approach: They propose to use the Universal Dependencies annotation scheme to annotate spoken language treebanks using a morphosyntactic annotation scheme.
Outcome: The proposed treebanks differ significantly with respect to the inventory and format of transcribed phenomena and the principles adopted in their morphosyntactic annotation.
75 Languages, 1 Model: Parsing Universal Dependencies Universally (D19-1)

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Challenge: UDify is a multilingual multi-task model that can predict universal part-of-speech, morphological features, lemmas, and dependency trees.
Approach: They evaluate UDify, a multilingual multi-task model capable of predicting universal part-of-speech, morphological features, lemmas, and dependency trees simultaneously for all 124 Universal Dependencies treebanks across 75 languages.
Outcome: The proposed model can predict universal part-of-speech, morphological features, lemmas, and dependency trees for all 124 treebanks across 75 languages.
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.
Climbing the Tower of Treebanks: Improving Low-Resource Dependency Parsing via Hierarchical Source Selection (2021.findings-acl)

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Challenge: Recent work on multilingual dependency parsing focused on developing highly multilingual parsers . a recent major paradigm shift in NLP towards largescale pretrained language models has reduced the downstream relevance of supervised syntactic parse.
Approach: They propose a heuristic approach to multilingual dependency parsing that heurs out the "one model to rule them all" approach by hierarchically clustering all Universal Dependencies languages based on their syntactic similarity .
Outcome: The proposed approach outperforms a "one model to rule them all" approach with a heuristic selection of languages and treebanks for a target language.
Investigating UD Treebanks via Dataset Difficulty Measures (2023.eacl-main)

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Challenge: Treebanks annotated with Universal Dependencies (UD) are currently available for over 100 languages and are only partially reflected in parser evaluations via accuracy metrics like LAS.
Approach: They propose to use dataset cartography, V-information, and minimum description length to analyze UD treebanks using three accuracy-free methods to provide insights about them.
Outcome: The proposed methods provide insights about UD treebanks that would remain undetected if only LAS was considered.
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).
Cross-Lingual Dependency Parsing Using Code-Mixed TreeBank (D19-1)

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Challenge: Treebank translation is a promising method for cross-lingual transfer of syntactic dependency knowledge.
Approach: They propose to map dependency arcs from source treebank to target translation according to word alignments.
Outcome: Experiments on university dependency treebanks show that translated treebank translations are more effective than translated treebans.

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