| 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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Low-Resource Syntactic Transfer with Unsupervised Source Reordering (N19-1)
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| Challenge: | Existing methods for dependency parsing use word order differences between source and target languages. |
| Approach: | They propose a cross-lingual transfer method that takes into account word order differences between source and target languages. |
| Outcome: | The proposed method improves on 68 treebanks (38 languages) on a target language. |
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). |
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 . |
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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. |
Cross-lingual Parsing with Polyglot Training and Multi-treebank Learning: A Faroese Case Study (D19-61)
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| Challenge: | Cross-lingual dependency parsing involves transferring syntactic knowledge from one language to another. |
| Approach: | They compare two approaches to cross-lingual dependency parsing using monolingual source models and a polyglot model which is trained on the combination of all source languages. |
| Outcome: | The proposed methods improve low-resource dependency parsers by transferring syntactic knowledge from one language to another. |
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. |
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. |
Universal Dependency Parsing for Hindi-English Code-Switching (N18-1)
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| Challenge: | Code-switching data often need additional processes such as language identification, normalization and/or back-transliteration to be processed. |
| Approach: | They propose a neural stacking model that leverages part-of-speech tags and syntactic tree annotations in tweets to parse code-switching data. |
| Outcome: | The proposed model is 1.5% better than the augmented model and 3.8% better than one which uses first-best normalization and/or back-transliteration. |
Some Languages Seem Easier to Parse Because Their Treebanks Leak (2020.emnlp-main)
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| Challenge: | Cross-language differences in (universal) dependency parsing performance are mostly attributed to treebank size, average sentence length, average dependency length, morphological complexity, and domain differences. |
| Approach: | They compute graph isomorphisms and find that treebank size is a factor that influences parsing performance. |
| Outcome: | The results show that the overlap between training and test graphs explain more of the observed variation than standard explanations such as the above. |