Universal Dependencies v1: A Multilingual Treebank Collection

By Joakim Nivre, Marie-Catherine de Marneffe, Filip Ginter, Yoav Goldberg, Jan Hajic, Christopher D. Manning, Ryan T. McDonald, Slav Petrov, Sampo Pyysalo, Natalia Silveira and 2 more. 2016

1,486 citations recorded by the source corpus.

Cross-linguistically consistent annotation is necessary for sound comparative evaluation and cross-lingual learning experiments. It is also useful for multilingual system development and comparative linguistic studies. Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework. In this paper, we describe v1 of the universal guidelines, the underlying design principles, and the currently available treebanks for 33 languages.

Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages (2020, background): “…that no single effort will be sufficient to cover the world’s languages, and so we invite others to create compatible datasets for other languages; the universal dependency treebank (Nivre et al., 2016) now has over 70 languages, demonstrating what the community is capable of with broad effort.”
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022, background): “, 2018) probing setup and datasets for each language available in Universal Dependencies (UD; Nivre et al., 2016).”
Tailor: Generating and Perturbing Text with Semantic Controls (2021, methodology): “See §B for more details. tasks: boolean question answering (BoolQ: Clark et al., 2019), extractive QA (SQuAD: Rajpurkar et al., 2016), dependency tree parsing (UD English: Nivre et al., 2016), and temporal relation extraction (MATRES: Ning et al., 2018).”
Colorless Green Recurrent Networks Dream Hierarchically (2018, neutral): “0, Nivre et al., 2016).”
Evaluating Models’ Local Decision Boundaries via Contrast Sets (2020, methodology): “UD Parsing Finally, we discuss dependency parsing in the universal dependencies (UD) formalism (Nivre et al., 2016).”
How Multilingual is Multilingual BERT? (2019, methodology): “We perform POS experiments using Universal De-pendencies (UD) (Nivre et al., 2016) data for 41 languages.”
Evaluating NLP Models via Contrast Sets (2020, methodology): “…in Section A): • NLVR2 (Suhr et al., 2019) • IMDb sentiment analysis (Maas et al., 2011) • MATRES Temporal RE (Ning et al., 2018) • English UD parsing (Nivre et al., 2016) • PERSPECTRUM (Chen et al., 2019) • DROP (Dua et al., 2019) • Quoref (Dasigi et al., 2019) • ROPES (Lin et al., 2019) •…”
Results of the WMT17 Metrics Shared Task (2017, methodology): “The language-universal A UTO DA uses seven features based on word-aligned parse trees in Universal Dependencies style (Nivre et al., 2016).”
A Survey of Cross-lingual Word Embedding Models (2017, methodology): “Along the same line, the recent initiative on providing cross-linguistically consistent sets of such labels (e.g., Universal Dependencies, Nivre et al., 2016) facilitates cross-lingual transfer and offers further support to the induction of word embeddings across languages (Vulić, 2017; Vulić, Schwa”
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction (2018, methodology): “For both datasets, we work with the Universal Dependencies v1 formalism (Nivre et al., 2016).”
When is multitask learning effective? Semantic sequence prediction under varying data conditions (2016, neutral): “3 (Nivre et al., 2016).”
Women’s Syntactic Resilience and Men’s Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing (2019, methodology): “4 (Nivre et al., 2016) annotation guidelines for parse trees and POS tags, and accordingly, convert the constituency trees from the Penn Treebank (PTB) format to the CoNNL format.3 We then map the POS tags to the universal POS tag set.4”
Emergent linguistic structure in artificial neural networks trained by self-supervision (2020, background): “Such datasets of annotated human language structure, known as treebanks (4, 5), have fueled much of the research in the field in the last 25 y.”
Universal Dependencies (2021, methodology): “For these aspects we refer to the papers on UD v1 (Nivre et al. 2016) and v2 (Nivre et al. 2020) and to the UD Web site (https://universaldependencies.org).”
ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning (2023, background): “However, to our knowledge, none of existing work has conducted large-scale evaluations of ChatGPT for multiple and diverse languages/tasks as we do.”