Supervised Treebank Conversion: Data and Approaches (P18-1)

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Challenge: Existing work on treebank conversion focuses on unsupervised treebanks . lack of manually labeled data means that sentences have two syntactic trees at the same time.
Approach: They propose supervised treebank conversion using bi-tree aligned sentences . they propose two conversion approaches based on state-of-the-art deep biaffine parser .
Outcome: The proposed method outperforms the state-of-the-art deep biaffine parser on the English WSJ dataset by 0.97 (93.76% -92.79%)

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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.
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.
Dependency Parsing for Urdu: Resources, Conversions and Learning (2020.lrec-1)

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Challenge: Existing treebanks for Urdu are under-resourced due to lack of resources.
Approach: They propose to convert existing treebanks into a common format that is based on Universal Dependencies.
Outcome: The proposed format outperforms the MaltParser and a transition-based BiLSTM parser with word embeddings and significantly improves parsing accuracy.
The Persian Dependency Treebank Made Universal (2022.lrec-1)

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Challenge: Existing universal dependency treebanks are lacking sufficient annotated data.
Approach: They propose a method for converting Persian Dependency Treebank to Universal Dependencies using an automatic method.
Outcome: The proposed method is more compatible with Universal Dependencies than the Uppsala Persian Universal Dependency Treebank.
Scalable Cross-lingual Treebank Synthesis for Improved Production Dependency Parsers (2020.coling-industry)

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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.
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.
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.
Learned Incremental Representations for Parsing (2022.acl-long)

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Challenge: a new syntactic representation that commits to syntakic choices is proposed for humans . we use a system that uses only incremental processing of a prefix to predict the word in a sentence .
Approach: They propose a syntactic representation that commits to syntakic choices incrementally . they say the system can achieve 93.72 F1 on the Penn Treebank with as few as 5 bits per word .
Outcome: The proposed representation achieves 93.72 F1 on the Penn Treebank with as few as 5 bits per word . the analysis of the representations shows they have interpretable features and deferred resolution of syntactic ambiguities.
The Tembusu Treebank: An English Learner Treebank (2022.lrec-1)

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Challenge: a new treebank is created to help diagnose ungrammatical sentences using mal-rules . the Tembusu Learner Treebank is an open treebank created from the corpus of Learner English .
Approach: They propose to use the Tembusu Learner Treebank to train a new parse-ranking model for the English Resource Grammar . the model incorporates mal-rules in the annotation of ungrammatical sentences .
Outcome: The Tembusu Learner Treebank is an open treebank created from the NTU Corpus of Learner English . the treebank is unique for incorporating mal-rules in the annotation of ungrammatical sentences .

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