UNIVERSAL DEPENDENCIES

TermoUD - a language-independent terminology extraction tool (2023.eacl-demo)

Challenge:	MultiBLiMP 1.0 is a massively multilingual benchmark of linguistic minimal pairs covering 101 languages and 2 types of subject-verb agreement.
Approach:	They propose to use multilingual benchmarks to evaluate linguistic minimal pairs in 101 languages and 2 types of subject-verb agreement to create the minimal pairs.
Outcome:	The proposed benchmark covers 101 languages and 2 types of subject-verb agreement, and contains more than 128,000 minimal pairs.

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Malgorzata Marciniak, Piotr Rychlik, Agnieszka Mykowiecka

Sentence Identification with BOS and EOS Label Combinations (2023.findings-eacl)

Challenge:	TermoUD is a language-independent terminology extraction tool . it uses languagedependent shallow grammar to select candidate terms .
Approach:	They propose a language-independent terminology extraction tool called TermoUD which uses shallow grammar to select candidate terms.
Outcome:	The proposed method is suitable for languages with the Universal Dependencies (UD) parser.

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Takuma Udagawa, Hiroshi Kanayama, Issei Yoshida

What Does Parameter-free Probing Really Uncover? (2024.acl-short)

Challenge:	Existing methods for preprocessing sentences only use the end of the sentence (EOS) however, real-world texts often contain non-sentential units (NSUs) such as metadata, sentence fragments, etc.
Approach:	They propose a task of sentence identification where the goal is to identify SUs while excluding NSUs in a given text.
Outcome:	The proposed method outperforms baselines which only use EOS labels on the sentence identification task.

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Tommi Buder-Gröndahl

Multitask Parsing Across Semantic Representations (P18-1)

Challenge:	Probing large language models (LLMs) has been criticized for using pre-defined label-laden target labels.
Approach:	They extend a parameter-free probing technique called perturbed masking applied to BERT to examine the relationship between UD and BERT.
Outcome:	The proposed method is compared to the UD formalism for English and shows that it lacks correlations with linguistic theory.

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Daniel Hershcovich, Omri Abend, Ari Rappoport

Joint Universal Syntactic and Semantic Parsing (2021.tacl-1)

Challenge:	UCCA parsing is a test case for multitask learning, with auxiliary tasks AMR, SDP and Universal Dependencies (UD) . Semantic parsers have arguably yet to reach their full potential due to the limited amount of semantically annotated training data.
Approach:	They propose a general transition-based parser that can parse UCCA, AMR, SDP and Universal Dependencies (UD) they use a transition-driven learning architecture and a uniform transition-basic learning architecture to train the parsers.
Outcome:	The proposed parser improves UCCA, AMR, SDP and Universal Dependencies (UD) parsing over training in English, German and French.

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Elias Stengel-Eskin, Kenton Murray, Sheng Zhang, Aaron Steven White, Benjamin Van Durme

Content Differences in Syntactic and Semantic Representation (N19-1)

Challenge:	Several attempts have been made to jointly parse syntax and semantics, but this trade-off is not well understood.
Approach:	They propose multiple model architectures that exploit the rich syntactic and semantic annotations contained in the Universal Decompositional Semantics dataset to obtain state-of-the-art results.
Outcome:	The proposed model outperforms existing models in 8 languages and their results are consistent across languages.

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Daniel Hershcovich, Omri Abend, Ari Rappoport

Improving Cross-lingual Transfer through Subtree-aware Word Reordering (2023.findings-emnlp)

Challenge:	Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate.
Approach:	They propose to use Universal Dependencies and UCCA as test cases to compare syntactic and semantic schemes.
Outcome:	The proposed comparison methodology can be used for fine-grained evaluation of UCCA parsing, highlighting both challenges and potential sources for improvement.

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Ofir Arviv, Dmitry Nikolaev, Taelin Karidi, Omri Abend

I Speak for the Árboles: Developing a Dependency Treebank for Spanish L2 and Heritage Speakers (2025.acl-srw)

Challenge:	Recent studies show that multilingual language models are not effective when dealing with less-represented languages.
Approach:	They propose a powerful reordering method that learns word-order patterns conditioned on the syntactic context from a small amount of annotated data.
Outcome:	The proposed method outperforms baselines on a variety of tasks and is effective in both zero-shot and few-shot scenarios.

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Emiliana Pulido, Robert Pugh, Zoey Liu

Aligning the Norwegian UD Treebank with Entity and Coreference Information (2024.lrec-main)

Challenge:	Existing dependency treebanks for learner writing are limited due to morphosyntactic features.
Approach:	They propose to use a dependency treebank for Spanish learner writing from the UC Davis COWSL2H corpus to incorporate lemmatization, POS tagging, and syntactic dependencies.
Outcome:	The proposed treebanks are openly accessible to motivate future development of learner-oriented language technologies.

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Tollef Emil Jørgensen, Andre Kåsen

A Universal Dependencies Treebank for Highland Puebla Nahuatl (2024.naacl-long)

Challenge:	merged corpora of entity and coreference data are presented for the two written forms of Norwegian: Bokml and Nynorsk.
Approach:	They propose to combine entity and coreference data from two UD treebanks for Norwegian written forms: Bokml and Nynorsk.
Outcome:	The merged corpora comprise the first Norwegian UD treebank enriched with named entities and coreference information, supporting the standardized format for the CorefUD initiative.

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Robert Pugh, Francis Tyers

Investigating UD Treebanks via Dataset Difficulty Measures (2023.eacl-main)

Challenge:	Annotated linguistic corpora are essential component of natural language processing (NLP) Annotation frameworks are used for morphological and dependency-based syntactic phenomena in endangered, indigenous, and/or marginalized languages.
Approach:	They propose a Universal Dependencies (UD) treebank for Highland Puebla Nahuatl . they describe the process of data collection, annotation decisions and interesting syntactic constructions .
Outcome:	The proposed treebank is the second such UD treebank for a Mexican language . it is a significant addition to an existing treebank of another Nahuatl language based on the framework .

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Artur Kulmizev, Joakim Nivre

Robust Multilingual Part-of-Speech Tagging via Adversarial Training (N18-1)

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.

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Michihiro Yasunaga, Jungo Kasai, Dragomir Radev

Memory-enhanced Large Language Model for Cross-lingual Dependency Parsing via Deep Hierarchical Syntax Understanding (2025.findings-emnlp)

Challenge:	Adversarial training (AT) is a powerful regularization method for neural networks, aiming to achieve robustness to input perturbations.
Approach:	They propose and analyze a neural POS tagging model that exploits adversarial training by training on unmodified and adversarials.
Outcome:	The proposed model improves overall tagging accuracy and prevents over-fitting in low resource languages and boosts tabbing accuracy for rare / unseen words.

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Jianjian Liu, Ying Li, Zhengtao Yu, Shun Su, Shengxiang Gao, Yuxin Huang

Fine-Grained Analysis of Cross-Linguistic Syntactic Divergences (2020.acl-main)

Challenge:	Experimental results show that our approach can significantly improve the parsing accuracy of all baseline models, leading to new state-of-the-art results.
Approach:	They propose a deep hierarchical syntax understanding approach to improve the cross-lingual semantic memory capability of large language models by implicitly aligning linguistic knowledge between source and target languages.
Outcome:	The proposed approach improves the cross-lingual semantic memory capability of large language models by combining implicit multi-task fine-tuning and explicit label bank guiding.

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Dmitry Nikolaev, Ofir Arviv, Taelin Karidi, Neta Kenneth, Veronika Mitnik, Lilja Maria Saeboe, Omri Abend

“Voices of the Great War”: A Richly Annotated Corpus of Italian Texts on the First World War (2020.lrec-1)

Challenge:	Existing work on quantifying the prevalence of syntactic divergences across languages has not been done.
Approach:	They propose a framework for extracting divergence patterns for any language pair from a parallel corpus building on Universal Dependencies.
Outcome:	The proposed framework provides a detailed picture of cross-language divergences, generalizes previous approaches, and lends itself to full automation.

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Federico Boschetti, Irene De Felice, Stefano Dei Rossi, Felice Dell’Orletta, Michele Di Giorgio, Martina Miliani, Lucia C. Passaro, Angelica Puddu, Giulia Venturi, Nicola Labanca, Alessandro Lenci, Simonetta Montemagni

Multimodality

Challenge:	“Voices of the Great War” is the first large corpus of Italian historical texts dating back to the period of First World War.
Approach:	"Voices of the Great War" is the first large corpus of Italian historical texts dating back to the period of First World War.
Outcome:	The "Voices of the Great War" corpus is the first large corpus of Italian historical texts dating back to the period of First World War.

Syntactic Nuclei in Dependency Parsing – A Multilingual Exploration (2021.eacl-main)

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Ali Basirat, Joakim Nivre

A New Latin Treebank for Universal Dependencies: Charters between Ancient Latin and Romance Languages (2020.lrec-1)

Challenge:	Existing models for syntactic dependency parsing assume words are elementary units that enter into dependency relations.
Approach:	They propose to use composition functions to make a transition-based dependency parser aware of the notion of nucleus.
Outcome:	The proposed concept of nucleus gives small but significant improvements in parsing accuracy on 12 languages.

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Flavio Massimiliano Cecchini, Timo Korkiakangas, Marco Passarotti

Towards Universal Segmentations: UniSegments 1.0 (2022.lrec-1)

Challenge:	Currently, Latin features the most data and the most treebanks of all the ancient languages of UD .
Approach:	They introduce a Latin treebank that follows the Universal Dependencies (UD) annotation standard . they use a translation of the late Latin Charter Treebank 2 (LLCT2) into the UD style .
Outcome:	The proposed treebank is based on the Universal Dependencies (UD) annotation standard.

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Zdeněk Žabokrtský, Niyati Bafna, Jan Bodnár, Lukáš Kyjánek, Emil Svoboda, Magda Ševčíková, Jonáš Vidra

Composable Sparse Fine-Tuning for Cross-Lingual Transfer (2022.acl-long)

Challenge:	Existing data resources for morphological segmentation are limited to 32 languages . a large number of word forms exist, with some sub-parts being "recycled" many times .
Approach:	They propose a multilingual data resource for morphological segmentation in 32 languages . they analyze diversity of how individual linguistic phenomena are captured across them .
Outcome:	The proposed scheme is based on 17 existing data resources relevant for segmentation in 32 languages.

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Alan Ansell, Edoardo Ponti, Anna Korhonen, Ivan Vulić

Twitter Universal Dependency Parsing for African-American and Mainstream American English (P18-1)

Challenge:	Adapters and sparse fine-tuning have been developed to improve transfer learning . a number of approaches have been proposed to improve performance of fine-untuners .
Approach:	They propose a method that fine-tunes the entire set of parameters of a large pretrained model . they use adapters and sparse fine-uning to improve model efficiency .
Outcome:	The proposed method outperforms adapters in cross-lingual transfer benchmarks.

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Su Lin Blodgett, Johnny Wei, Brendan O’Connor

Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP (P18-1)

Challenge:	We analyze the performance disparities between AAE and Mainstream American English (MAE) because of Twitter-specific conventions and dialectal language.
Approach:	They develop a dataset of 500 tweets, 250 of which are in AAE, within the Universal Dependencies 2.0 framework and annotate it.
Outcome:	The proposed model improves performance for AAE tweets with no or very little in-domain labeled data and assesses its lexical and syntactic features.

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Edoardo Maria Ponti, Roi Reichart, Anna Korhonen, Ivan Vulić

A Treebank of Asia Minor Greek (2024.lrec-main)

Challenge:	Using frameworks such as Universal Dependencies (UD) to transfer knowledge between languages can be challenging because of variation in syntactic structures.
Approach:	They propose a typologically driven method which reduces anisomorphism in UD treebanks by considering both morphological and structural properties.
Outcome:	The proposed method is effective for machine translation and cross-lingual sentence similarity.

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Eleni Vligouridou, Inessa Iliadou, Çağrı Çöltekin

Valency-Augmented Dependency Parsing (D18-1)

Challenge:	Asia Minor Greek (AMG) dialects are endangered because of declining speaker base and scarce linguistic resources.
Approach:	They propose to annotate a treebank of Pharasiot Greek, one of the Asia Minor Greek (AMG) dialects.
Outcome:	The proposed treebank consists of 350 sentences from six fairy tales in Pharasiot Greek.

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Tianze Shi, Lillian Lee

Dependency Parsing for Spoken Dialog Systems (D19-1)

Challenge:	valency analysis is a complex task that requires a large number of subcategorizations, such as the number and types of syntactic dependents.
Approach:	They propose a parsing approach that explicitly models the number and types of syntactic dependents as valency patterns and a probabilistic model for tagging them.
Outcome:	The proposed approach outperforms the state-of-the-art labeled attachment score on 53 treebanks representing 41 languages and outperformed the previous state- of-the art labeles by 0.7.

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Sam Davidson, Dian Yu, Zhou Yu

Dependency Parsing as MRC-based Span-Span Prediction (2022.acl-long)

Challenge:	Dependency parsing of conversational input can help to understand dialogs . currently available annotation schemes do not adapt well to spoken human-machine dialogs.
Approach:	They propose an annotation scheme that extends Universal Dependencies guidelines to spoken dialogs.
Outcome:	The proposed scheme disambiguates relationships between entities extracted from dialogs . it is better than existing models on public datasets and fine-tuned on ConvBank data .

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Leilei Gan, Yuxian Meng, Kun Kuang, Xiaofei Sun, Chun Fan, Fei Wu, Jiwei Li

Spoken Language Treebanks in Universal Dependencies: an Overview (2022.lrec-1)

Challenge:	Existing methods for dependency parsing address the issue that edges should be constructed at the text span/subtree level rather than word level.
Approach:	They propose a method that constructs dependency trees by directly modeling span-span relations by modeling subtree-subtree relationships.
Outcome:	The proposed method constructs dependency trees by modeling span-span relations . it can retrieve missing spans in the span proposal stage, which leads to higher recall .

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Kaja Dobrovoljc

Evaluating Morphological Plausibility of Subword Tokenization via Statistical Alignment with Morpho-Syntactic Features (2026.findings-eacl)

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.

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Abishek Stephen, Jindřich Libovický

Contextualization of Morphological Inflection (N19-1)

Challenge:	Existing metric for subword tokenization evaluation for morphological plausibility requires unavailable or inconsistent gold segmentation data.
Approach:	They propose a morpho-syntactic feature-based metric for subword tokenization evaluation.
Outcome:	The proposed metric correlates well with traditional morpheme boundary recall while being more broadly applicable across languages with different morphological systems.

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Ekaterina Vylomova, Ryan Cotterell, Trevor Cohn, Timothy Baldwin, Jason Eisner

Morphology

Challenge:	In this paper, we isolate the task of predicting a fully inflected sentence from its partially lemmatized version.
Approach:	They propose a task that requires morphological features to be inferred from sentential context . they propose morphology-based models that explicitly reconstruct morphologic features before predicting inflected forms .
Outcome:	The proposed model is able to predict inflected sentences without relying on morphological annotations.

Universal Dependencies According to BERT: Both More Specific and More General (2020.findings-emnlp)

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Tomasz Limisiewicz, David Mareček, Rudolf Rosa

A Structured Variational Autoencoder for Contextual Morphological Inflection (P18-1)

Challenge:	Existing studies show that individual BERT heads encode particular dependency relation types, but they do not match one-to-one.
Approach:	They propose a method for relation identification and syntactic tree construction that can be applied with minimal supervision and generalizes well across languages.
Outcome:	The proposed method produces significantly more consistent dependency trees than previous work and can be applied with only a minimal amount of supervision and generalizes well across languages.

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Lawrence Wolf-Sonkin, Jason Naradowsky, Sabrina J. Mielke, Ryan Cotterell

Neural Factor Graph Models for Cross-lingual Morphological Tagging (P18-1)

Challenge:	morphological inflectors typically trained on fully supervised, type-level data, but how can we improve their performance? et al., 2016: a novel latent-variable model for semi-supervised learning of inflection generation.
Approach:	They propose a latent-variable model for semi-supervised learning of inflection generation . they use a wake-sleep algorithm to enable posterior inference over latent variables .
Outcome:	The proposed model improves on 23 languages and shows 10% accuracy improvement . the proposed model is based on the wake-sleep algorithm .

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Chaitanya Malaviya, Matthew R. Gormley, Graham Neubig

Ensemble Romanian Dependency Parsing with Neural Networks (L18-1)

Challenge:	Existing approaches to morphological tagging are limited by the assumption that tag sets overlap . a limited amount of data is available for most languages to learn these morphology taggers.
Approach:	They propose a method for cross-lingual morphological tagging that relaxes this assumption . they use factorial conditional random fields with neural network potentials to smooth over superficial differences in the surface forms .
Outcome:	The proposed model can smooth over superficial differences in the surface forms and generate unseen or rare tag sets.

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Radu Ion, Elena Irimia, Verginica Barbu Mititelu

A Universal Dependencies Treebank of Ancient Hebrew (2022.lrec-1)

Challenge:	SSPR is a Python 3.5 application based on the Microsoft Cognitive Toolkit 2.0 Python API.
Approach:	a Python 3.5 application is based on the Microsoft Cognitive Toolkit 2.0 Python API.
Outcome:	SSPR outperforms the best individual parser at the CONLL 2017 dependency parsing shared task.

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Daniel Swanson, Francis Tyers

Comparing learnability of two dependency schemes: ‘semantic’ (UD) and ‘syntactic’ (SUD) (2021.findings-emnlp)

Challenge:	Using a rule-based parser, we construct a treebank with morphological annotations of Ancient Hebrew . the Hebrew Scriptures are a collection of 39 books written in the first millennium BC in Ancient Hebrew.
Approach:	They propose to use a Universal Dependencies treebank with morphological annotations of Ancient Hebrew for comparative study with ancient translations and analysis of Hebrew syntax.
Outcome:	The proposed treebank can be used in comparative study with ancient translations and analysis of Hebrew syntax.

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Ryszard Tuora, Adam Przepiórkowski, Aleksander Leczkowski

Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging (2022.findings-acl)

Challenge:	Several studies have suggested that choosing syntactic criteria for assigning heads in dependency trees improves the performance of dependency parsers.
Approach:	They propose to use syntactic criteria to assign heads to dependency trees to improve the performance of dependency parsers by using a selection of 21 treebanks.
Outcome:	The proposed approach favours content words over function words as heads of dependency relations, while the other favours syntactic heads.

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Houquan Zhou, Yang Li, Zhenghua Li, Min Zhang

Is Supervised Syntactic Parsing Beneficial for Language Understanding Tasks? An Empirical Investigation (2021.eacl-main)

Challenge:	Large-scale pre-trained language models (PLMs) have made extraordinary progress in most NLP tasks, but they fail to achieve state-of-the-art (SOTA) performance.
Approach:	They propose a Guassian HMM variant for unsupervised POS tagging that incorporates contexualized word representations into the decoder.
Outcome:	The proposed model outperforms state-of-the-art models on Penn Treebank and multilingual Universal Dependencies treebank v2.0.

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Goran Glavaš, Ivan Vulić

PoSTWITA-UD: an Italian Twitter Treebank in Universal Dependencies (L18-1)

Challenge:	Traditional NLP has long held (supervised) syntactic parsing necessary for successful higher-level semantic language understanding (LU).
Approach:	They empirically examine the usefulness of supervised parsing for semantic LU in LM-pretrained transformer networks.
Outcome:	The proposed model is based on LM-pretrained transformer networks with a biaffine parsing head and fine-tuned for LU tasks.

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Manuela Sanguinetti, Cristina Bosco, Alberto Lavelli, Alessandro Mazzei, Oronzo Antonelli, Fabio Tamburini

75 Languages, 1 Model: Parsing Universal Dependencies Universally (D19-1)

Challenge:	Various approaches and ad hoc resources are needed to provide proper coverage of specific linguistic phenomena.
Approach:	They propose to annotate tweets using a well-known dependency-based annotation format . they propose to use the tweets for training NLP systems to improve their performance .
Outcome:	The proposed resource can be used for training of NLP systems on social media texts.

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Dan Kondratyuk, Milan Straka

Universal Dependencies Version 2 for Japanese (L18-1)

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.

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Masayuki Asahara, Hiroshi Kanayama, Takaaki Tanaka, Yusuke Miyao, Sumire Uematsu, Shinsuke Mori, Yuji Matsumoto, Mai Omura, Yugo Murawaki

A Framework for Understanding the Role of Morphology in Universal Dependency Parsing (D18-1)

Challenge:	UD Japanese resources are built on automatic conversion from several treebanks.
Approach:	They propose to port the word delimitation, POS, and syntactic relations of existing treebanks to UD Japanese . they discuss the issues of the UD scheme found through porting of the Japanese language .
Outcome:	The proposed UD Japanese resources are based on automatic conversion from treebanks.

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Mathieu Dehouck, Pascal Denis

Constructing a Dependency Treebank for Second Language Learners of Korean (2024.lrec-main)

Challenge:	a measure of morphological complexity is used to characterize syntactic information in word embeddings.
Approach:	They propose a measure of morphological complexity in terms of governor-dependent preferential attachment that explains parsing performance.
Outcome:	The proposed framework improves parsing performance on morphologically rich languages using morphology as a syntactic marker.

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Hakyung Sung, Gyu-Ho Shin

Building Universal Dependency Treebanks in Korean (L18-1)

Challenge:	a manually annotated syntactic treebank is available for second language learners . the dataset includes 7,530 sentences (66,982 words; 129,333 morphemes)
Approach:	They propose to manually annotate syntactic treebanks based on Universal Dependencies from Korean written data.
Outcome:	The proposed dataset includes 7,530 sentences and 129,333 morphemes from Korean learners.

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Jayeol Chun, Na-Rae Han, Jena D. Hwang, Jinho D. Choi

Universal Dependencies for Amharic (L18-1)

Challenge:	Several treebanks were introduced for Korean, all of which comprised annotation of morphemes and phrase structure trees, each following its own set of guidelines.
Approach:	They propose to use Korean treebanks as dependency trees and to analyze their performance using morpheme-level annotations.
Outcome:	The Korean portion of the Google UD Treebank, the Penn Korean Treebank and the KAIST Treebank are re-tokenized and assessed for errors.

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Binyam Ephrem Seyoum, Yusuke Miyao, Baye Yimam Mekonnen

Real-Valued Logics for Typological Universals: Framework and Application (2020.coling-main)

Challenge:	Amharic is a morphologically rich language with a dependency relation between orthographic words and lexical categories.
Approach:	They propose to create an Amharic Dependency Treebank by POS tagging, morphological information and dependency relations.
Outcome:	The proposed treebanks are based on 1,096 sentences and are able to parse Amharic.

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Tillmann Dönicke, Xiang Yu, Jonas Kuhn

Creating a Parallel Icelandic Dependency Treebank from Raw Text to Universal Dependencies (2020.lrec-1)

Challenge:	a framework for typological statements captures the truth value of a formula on a given data source.
Approach:	They propose a framework which captures the empirical truth value of a formula on a given data source.
Outcome:	The proposed framework can be used to express typological statements on multilingual treebanks with comparable annotation.

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Hildur Jónsdóttir, Anton Karl Ingason

Universal Dependencies for Ainu (L18-1)

Challenge:	Icelandic language is low-resource and is not yet considered in imminent danger . efforts underway to make it accessible and usable in Language Technology .
Approach:	They propose to build a parallel Icelandic dependency treebank based on Universal Dependencies (UD) this is the first parallel treebank resource for the language and several other languages already have one .
Outcome:	The proposed treebank is the first parallel treebank resource for the low-resource language . the project will be published as part of UD version 2.6.

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Hajime Senuma, Akiko Aizawa

Do Neural Language Models Show Preferences for Syntactic Formalisms? (2020.acl-main)

Challenge:	a task is underway to create a dependency tree bank for the Ainu language in the scheme of Universal Dependencies (UD).
Approach:	They propose to create a dependency tree bank for the Ainu language in the scheme of Universal Dependencies (UD) their mini-lexicon is encoded under the W3C OntoLex specification with UD and UniMorph features with the system-friendly JSON-LD format and is bearable to future extensions.
Outcome:	The proposed tree bank contains 10,000 word tokens and is small enough to be used as a base annotation for the next step.

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Artur Kulmizev, Vinit Ravishankar, Mostafa Abdou, Joakim Nivre

Low-Resource Syntactic Transfer with Unsupervised Source Reordering (N19-1)

Challenge:	Recent work on interpretability of deep neural language models concludes that many properties of natural language syntax are encoded in their representational spaces.
Approach:	They propose to examine whether syntactic structure adheres to a surface-syntactical or deep syntaktic style of analysis.
Outcome:	The proposed model prefers Universal Dependencies (UD) over Surface-Syntactic Universal Dependency (SUD) with interesting variations across languages and layers.

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Mohammad Sadegh Rasooli, Michael Collins

A Pointer Network Architecture for Joint Morphological Segmentation and Tagging (2020.findings-emnlp)

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.

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Amit Seker, Reut Tsarfaty

On the Relation between Syntactic Divergence and Zero-Shot Performance (2021.emnlp-main)

Challenge:	Morphological Disambiguation (MD) is a task of decomposing tokens into morphemes . a simple pipeline is used to segment and tagging raw tokens .
Approach:	They propose a new pointer network model that combines symbolic knowledge of morphemes with the learning capacity of neural end-to-end modeling.
Outcome:	The proposed model outperforms all previous reported results on Hebrew and Turkish . it uses morphological knowledge and the learning capacity of neural end-to-end modeling .

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Ofir Arviv, Dmitry Nikolaev, Taelin Karidi, Omri Abend

Building an Endangered Language Resource in the Classroom: Universal Dependencies for Kakataibo (2022.lrec-1)

Challenge:	Recent advances in cross-lingual transfer methods have enabled significant advances in grammatical processing tasks.
Approach:	They examine the extent to which syntactic relations are preserved in translation and parsability in a zero-shot setting.
Outcome:	The proposed model is based on a translation task in English and a subset of a standard English RE benchmark translated to Russian and Korean.

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Roberto Zariquiey, Claudia Alvarado, Ximena Echevarría, Luisa Gomez, Rosa Gonzales, Mariana Illescas, Sabina Oporto, Frederic Blum, Arturo Oncevay, Javier Vera

Dependencies over Times and Tools (DoTT) (2024.lrec-main)

Challenge:	12 This paper describes the collaborative methodology implemented to create a UD treebank for a Peruvian endangered language.
Approach:	They propose to create a UD treebank for a Peruvian endangered language . they use a collaborative methodology to create the treebank in a course .
Outcome:	The proposed treebank would enhance the future development of an NLP toolkit for this endangered language.

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Andy Luecking, Giuseppe Abrami, Leon Hammerla, Marc Rahn, Daniel Baumartz, Steffen Eger, Alexander Mehler

Challenge:	Using the examples of English and German, we examine how parsers trained on modern variants of these languages can be transferred to older language levels without loss.
Approach:	They develop a treebank of diachronic corpora enriched with dependency annotations using 3 parsers, 6 pre-trained language models, 5 newly trained models for German, and two tag sets.
Outcome:	The proposed treebank covers the time period from 1800 until today and is based on the DependencyAnnotator annotation tool.

Universal Morphologies for the Caucasus region (L18-1)

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Christian Chiarcos, Kathrin Donandt, Maxim Ionov, Monika Rind-Pawlowski, Hasmik Sargsian, Jesse Wichers Schreur, Frank Abromeit, Christian Fäth

Climbing the Tower of Treebanks: Improving Low-Resource Dependency Parsing via Hierarchical Source Selection (2021.findings-acl)

Challenge:	Caucasus region is famed for its rich and diverse arrays of languages and language families . authors describe efforts to improve the coverage of Universal Morphologies for languages of the region .
Approach:	They propose to improve the coverage of Universal Morphologies for Caucasus languages . they propose to complement the Universal Dependencies which focus on morphosyntax and syntax.
Outcome:	The proposed framework improves the coverage of languages of the Caucasus region . the proposed framework criticizes the UniMorph TSV format for its limited expressiveness .

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Goran Glavaš, Ivan Vulić

TwittIrish: A Universal Dependencies Treebank of Tweets in Modern Irish (2022.acl-long)

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.

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Lauren Cassidy, Teresa Lynn, James Barry, Jennifer Foster

Yet Another Format of Universal Dependencies for Korean (2022.coling-1)

Challenge:	Modern Irish is a minority language lacking computational resources for accurate automatic syntactic parsing of user-generated content.
Approach:	They propose to use a treebank to facilitate natural language parsing of user-generated content in Irish.
Outcome:	The proposed treebank enables natural language processing of user-generated content in Irish.

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Yige Chen, Eunkyul Leah Jo, Yundong Yao, KyungTae Lim, Miikka Silfverberg, Francis M. Tyers, Jungyeul Park

Weakly Supervised Headline Dependency Parsing (2022.findings-emnlp)

Challenge:	Existing dependency parsers for Korean do not perform as well as their English counterparts due to the complexity of Korean's linguistic features.
Approach:	They propose a morpheme-based Korean dependency parsing format and propose to adopt it to Universal Dependencies.
Outcome:	The proposed format outperforms parsing results for Korean UD treebanks and detailed error analysis.

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Adrian Benton, Tianze Shi, Ozan İrsoy, Igor Malioutov

Finding Universal Grammatical Relations in Multilingual BERT (2020.acl-main)

Challenge:	English news headlines have unique syntactic properties documented in linguistics literature since the 1930s.
Approach:	They propose to provide the first news headline corpus of annotated syntactic dependency trees to evaluate existing NLP parsers on news headlines.
Outcome:	The proposed method improves performance across different news outlets, but is moderated by constructions idiosyncratic to outlet.

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Ethan A. Chi, John Hewitt, Christopher D. Manning

Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection (2020.lrec-1)

Challenge:	Recent work has found that multilingual masked language models learn a surprising amount of linguistic structure, despite a lack of direct linguistic supervision.
Approach:	They propose an unsupervised method to find syntactic tree distances in languages other than English and that these subspaces are approximately shared across languages.
Outcome:	The proposed method shows that mBERT learns representations of syntactic dependency labels, in the form of clusters, which largely agree with the Universal Dependencies taxonomy.

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Joakim Nivre, Marie-Catherine de Marneffe, Filip Ginter, Jan Hajič, Christopher D. Manning, Sampo Pyysalo, Sebastian Schuster, Francis Tyers, Daniel Zeman

How Universal are Universal Dependencies? Exploiting Syntax for Multilingual Clause-level Sentiment Detection (2020.lrec-1)

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.

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Hiroshi Kanayama, Ran Iwamoto

Tracing Syntactic Change in the Scientific Genre: Two Universal Dependency-parsed Diachronic Corpora of Scientific English and German (2022.lrec-1)

Challenge:	a new method for clause-level sentiment detection is proposed for multilingual use cases.
Approach:	They propose a pipeline method that makes the most of syntactic structures based on Universal Dependencies.
Outcome:	The proposed method achieves high precision in sentiment detection for 17 languages . it avoids machine-learning approaches that may cause obstacles to its use cases .

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Marie-Pauline Krielke, Luigi Talamo, Mahmoud Fawzi, Jörg Knappen

CorefUD 1.0: Coreference Meets Universal Dependencies (2022.lrec-1)

Challenge:	a recent study has focused on the syntactic development of scientific discourse in English and German.
Approach:	They present two comparable diachronic corpora of scientific English and German from the Late Modern Period (17th c.–19th d.) annotated with Universal Dependencies.
Outcome:	The presented corpora are comparable to existing studies on grammatical change in English and German . the results show that the pre-processing steps significantly improve parsing accuracy .

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Anna Nedoluzhko, Michal Novák, Martin Popel, Zdeněk Žabokrtský, Amir Zeldes, Daniel Zeman

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

Challenge:	Recent advances in standardization for annotated language resources have led to successful large scale efforts, such as the Universal Dependencies (UD) project for multilingual syntactically annotized data.
Approach:	They propose a multilingual collection of corpora and a standardized format for coreference resolution compatible with morphosyntactic annotations in the UD framework.
Outcome:	The proposed framework is compatible with morphosyntactic annotations and includes facilities for related tasks such as named entity recognition.

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Tu-Anh Tran, Yusuke Miyao

A Broad-coverage Corpus for Finnish Named Entity Recognition (2020.lrec-1)

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.

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Jouni Luoma, Miika Oinonen, Maria Pyykönen, Veronika Laippala, Sampo Pyysalo

Universal Grammatical Dependencies for Portuguese with CINTIL Data, LX Processing and CLARIN support (2022.lrec-1)

Challenge:	Named entity recognition (NER) is a fundamental task in natural language processing (NLP).
Approach:	They propose to annotate Finnish named entity names using a new corpus built on the Universal Dependencies corpus.
Outcome:	The new annotation identifies over 10,000 mentions and maintains compatibility with a previously released single-domain corpus for Finnish NER.

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António Branco, João Ricardo Silva, Luís Gomes, João António Rodrigues

Universal Dependencies for Punjabi (2022.lrec-1)

Challenge:	a new collection of quality language resources is presented for the computational processing of the Portuguese language . the framework for the mapping between linguistic form and meaning is centered on the notion of grammatical relation .
Approach:	They propose a new set of quality language resources for the computational processing of the Portuguese language under the Universal Dependencies framework.
Outcome:	The proposed framework provides for the mapping between linguistic form and meaning representations.

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Aryaman Arora

Marking Irony Activators in a Universal Dependencies Treebank: The Case of an Italian Twitter Corpus (2020.lrec-1)

Challenge:	UD is a community project that maintains a standard scheme for the annotation of grammar in a cross-lingually consistent manner.
Approach:	They propose a Universal Dependencies treebank for Punjabi written in the Gurmukhi script and discuss corpus design and linguistic phenomena encountered in annotation.
Outcome:	The proposed treebank covers a variety of genres and has been annotated for POS tags, dependency relations, and graph-based Enhanced Dependencies.

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Alessandra Teresa Cignarella, Manuela Sanguinetti, Cristina Bosco, Paolo Rosso

Sentiment Analysis

Challenge:	Existing annotations for irony are difficult, and the recognition of it is difficult due to its polarity.
Approach:	They propose a fine-grained annotation scheme centered on irony that highlights the tokens responsible for its activation and their morpho-syntactic features.
Outcome:	The proposed scheme highlights the tokens responsible for irony activation and their morpho-syntactic features.

Yorùbá Dependency Treebank (YTB) (2020.lrec-1)

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Olájídé Ishola, Daniel Zeman

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

Challenge:	Low-resource languages present enormous NLP opportunities as well as varying degrees of difficulties.
Approach:	They propose to use the Yoruba Bible treebank to apply a new grammar formalism to the language by examining the use of universal dependency annotations.
Outcome:	The treebank of hand-annotated parts of the Yoruba Bible provides an avenue for dependency analysis of the language; the application of a new grammar formalism to the language.

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Toqeer Ehsan, Miriam Butt

Robert Vacareanu, George Caique Gouveia Barbosa, Marco A. Valenzuela-Escárcega, Mihai Surdeanu

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.

Parsing as Tagging (2020.lrec-1)

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Treebanking User-Generated Content: A Proposal for a Unified Representation in Universal Dependencies (2020.lrec-1)

Challenge:	Existing methods for dependency parsing treat parse as tagging, but they are not perfect.
Approach:	They propose a simple yet accurate method that treats parsing as tagging . they use a sequence model with a bidirectional LSTM over BERT embeddings .
Outcome:	The proposed method outperforms the state-of-the-art method on universal dependency (UD) by 1.76% unlabeled attachment score (UAS) for English, 1.98% UAS for French, and 1.16% UAS in German.

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Manuela Sanguinetti, Cristina Bosco, Lauren Cassidy, Özlem Çetinoğlu, Alessandra Teresa Cignarella, Teresa Lynn, Ines Rehbein, Josef Ruppenhofer, Djamé Seddah, Amir Zeldes

Representation Learning

Challenge:	Despite the increasing number of contributions on Part-of-Speech tagging and parsing, automatic processing of user-generated content (UGC) still represents a challenging task.
Approach:	They propose a set of guidelines for the annotation of user-generated texts within the Universal Dependencies framework.
Outcome:	The proposed annotation guidelines promote cross-linguistic consistency, which has always been in the spirit of UD.

When Collaborative Treebank Curation Meets Graph Grammars (2020.lrec-1)

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Gaël Guibon, Marine Courtin, Kim Gerdes, Bruno Guillaume

Towards the Conversion of National Corpus of Polish to Universal Dependencies (2020.lrec-1)

Challenge:	Arborator-Grew is a collaborative annotation tool for treebank development.
Approach:	They present a collaborative annotation tool for treebank development that combines the features of Arborator and Grew.
Outcome:	The proposed tool is a complete redevelopment and modernization of Arborator, replacing its internal database storage by a new Grew API.

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Alina Wróblewska

How does the task complexity of masked pretraining objectives affect downstream performance? (2023.findings-acl)

Challenge:	a paper aims at enriching the manually annotated part of National Corpus of Polish with a syntactic layer.
Approach:	They enrich manually annotated part of Polish National Corpus with a syntactic layer and a UD dependency graph.
Outcome:	The proposed model outperforms a model trained on a smaller set of gold-standard trees in predicting part-of-speech tags, morphological features, lemmata and labelled dependency trees.

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Atsuki Yamaguchi, Hiroaki Ozaki, Terufumi Morishita, Gaku Morio, Yasuhiro Sogawa

Annotation Interoperability for the Post-ISOCat Era (2020.lrec-1)

Challenge:	Masked language modeling (MLM) is a widely used self-supervised pretraining objective.
Approach:	They propose to use a mask-based objective to predict a token that is replaced with a masked token given its context.
Outcome:	The proposed objectives show that they should have half the complexity needed to perform comparably to MLM.

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Christian Chiarcos, Christian Fäth, Frank Abromeit

Knowledge Representation

Challenge:	Using ISOCat successor solutions, annotation standards have been developed since 2010 .
Approach:	They describe ISOCat successor solutions and annotation standardization efforts since 2010 . they describe low-cost harmonization of post-ISOCat vocabularies by means of linked ontologies .
Outcome:	The proposed ontologies are linked with the Ontologie of Linguistic Annotation and ISOCat, the GOLD ontology, the Typological Database Systems ontological and a large number of annotation schemes.

Adaptive Constraint Propagation: Scaling Structured Inference for Large Language Models via Meta-Reinforcement Learning (2026.acl-long)

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Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma

Creation of a Balanced State-of-the-Art Multilayer Corpus for NLU (L18-1)

Challenge:	Large language models increasingly require structured inference, says aaron sagar . meta-learning learns universal constraint propagation policies without task-specific training . standard schedulers are inexpensive but myopic, because they optimize local effects .
Approach:	MetaJuLS learns universal constraint propagation policies applicable across languages and tasks without task-specific retraining.
Outcome:	MetaJuLS achieves 1.5-2.0 speedups over GPU-optimized baselines while maintaining accuracy within 0.2% of state-of-the-art parsers.

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Normunds Gruzitis, Lauma Pretkalnina, Baiba Saulite, Laura Rituma, Gunta Nespore-Berzkalne, Arturs Znotins, Peteris Paikens

The Persian Dependency Treebank Made Universal (2022.lrec-1)

Challenge:	Using full stack of language resources, we are creating a balanced text corpus for Latvian.
Approach:	They propose to create a syntactically and semantically annotated multilayered corpus for Latvian . they use widely acknowledged and cross-lingual representations for the corpus .
Outcome:	The proposed corpus adopts widely recognized and cross-lingual representations for natural language understanding and generation in Latvian.

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Pegah Safari, Mohammad Sadegh Rasooli, Amirsaeid Moloodi, Alireza Nourian

Contribution of Linguistic Typology to Universal Dependency Parsing: An Empirical Investigation (2024.emnlp-main)

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.

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Ali Basirat, Navid Hemmati

Integrating Headedness Information into an Auto-generated Multilingual CCGbank for Improved Semantic Interpretation (2024.lrec-main)

Challenge:	UD is a global initiative to create a standard annotation for the dependency syntax of human languages.
Approach:	They propose a typologically motivated transformation of UD that emphasizes information packaging over lexical semantics.
Outcome:	The proposed scheme differs from previous attempts to create a universal annotation for human languages.

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Tu-Anh Tran, Yusuke Miyao

On the Continued Value of Universal Dependencies in the Era of Large Language Models (2026.acl-long)

Challenge:	Combinatory Categorial Grammar is a grammar formalism that provides a transparent interface between syntax and semantics.
Approach:	They propose an algorithm that adds semantic representations to existing CCG derivations by combining them with predefined combinatory rules.
Outcome:	The proposed method produces bare CCG derivations without any accompanying semantic representations and limits its general applicability.

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Wenxi Li, Jingyu Peng

Joint Annotation of Morphology and Syntax in Dependency Treebanks (2024.lrec-main)

Challenge:	a growing belief that explicit linguistic representations are no longer necessary is questioned in large language models . a recent study examines whether and in what ways this cross-lingual syntactic framework can still benefit LLMs .
Approach:	They use Universal Dependencies (UD) to examine whether and in what ways it can still benefit LLMs.
Outcome:	The proposed model outperforms its syntax-agnostic counterparts in a cross-lingual evaluation task.

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Bruno Guillaume, Kim Gerdes, Kirian Guiller, Sylvain Kahane, Yixuan Li

Killkan: The Automatic Speech Recognition Dataset for Kichwa with Morphosyntactic Information (2024.lrec-main)

Challenge:	Syntactic treebanks have been in development since the 1970s . they are now available for a vast array of languages from across the globe .
Approach:	They propose new formats to annotate syntactic and morphological relations in a dependency treebank using distributional criteria for the choice of the head of any combination.
Outcome:	The proposed formats are compatible with the UD schema for syntactic treebanks.

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Chihiro Taguchi, Jefferson Saransig, Dayana Velásquez, David Chiang

Speech & Audio in NLP

Challenge:	Existing datasets for automatic speech recognition (ASR) in the endangered Kichwa language have been limited.
Approach:	They present Killkan, the first dataset for automatic speech recognition (ASR) in the Kichwa language, an indigenous language of Ecuador.
Outcome:	The proposed dataset shows that it can be used to build an automatic speech recognition system for the endangered language with reliable quality despite its small size.

Parsing the Switch: LLM-Based UD Annotation for Complex Code-Switched and Low-Resource Languages (2025.findings-emnlp)

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Olga Kellert, Nemika Tyagi, Muhammad Imran, Nelvin Licona-Guevara, Carlos Gómez-Rodríguez

Low-Resource NLP

Challenge:	Existing approaches to analyzing code-switched data are limited in their ability to generalize to multilingual and mixed-language inputs.
Approach:	They propose a large-language model-based annotation pipeline to produce UD annotations for code-switched text.
Outcome:	The proposed pipeline outperforms existing parsers and baselines in syntactic analysis.

Conjunct Lengths in English, Dependency Length Minimization, and Dependency Structure of Coordination (2023.acl-long)

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Adam Przepiórkowski, Michał Woźniak

Challenge:	a lack of work on the left-to-right order of conjuncts in binary coordinations supports both leftness and closeness to the external head.
Approach:	They propose to explain this effect by minimizing the dependence between conjuncts and governors.
Outcome:	The proposed explanation provides support for symmetrical dependency structures, as opposed to asymmetrical structures.

A Tree Extension for CoNLL-RDF (2020.lrec-1)

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Christian Chiarcos, Luis Glaser

Structured Data in NLP

Challenge:	CoNLL-RDF provides a bridge for popular oneword-per-line formats . main reasons for their popularity are the simplicity of tables and tab-separated values .
Approach:	They propose a technology that provides a bridge between knowledge graphs and natural language processing.
Outcome:	The proposed technology provides a bridge for popular one-word-per-line formats . it provides native support for word-level annotations, but not phrase structures or text structure .

MaiBaam: A Multi-Dialectal Bavarian Universal Dependency Treebank (2024.lrec-main)

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Verena Blaschke, Barbara Kovačić, Siyao Peng, Hinrich Schütze, Barbara Plank

Parsing for Mauritian Creole Using Universal Dependencies (2024.lrec-main)

Challenge:	Despite the success of the Universal Dependencies (UD) project, there is still a lack of diversity within high-resource languages and their closely related non-standard languages and dialects.
Approach:	They propose to annotate Bavarian with part-of-speech and syntactic dependency information manually in UD and to highlight morphosyntactical differences between the closely related languages.
Outcome:	The proposed treebank covers multiple genres including wiki, fiction, grammar examples, social, non-fiction and Bavarian.

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Neha Ramsurrun, Rolando Coto-Solano, Michael Gonzalez

SUK 1.0: A New Training Corpus for Linguistic Annotation of Modern Standard Slovene (2024.lrec-main)

Challenge:	a paper demonstrates the construction of a 161-sentence treebank for Mauritian Creole . the parser trained with UD reached F1 scores of UPOS=86.2, UAS=80.8 and LAS=69.8.
Approach:	They propose to use Universal Dependencies to train a parser for Mauritian Creole . they demonstrate the construction of a 161-sentence treebank and evaluate the performance .
Outcome:	The proposed treebank achieves F1 scores compared to models for other under-resourced Creole languages.

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Špela Arhar Holdt, Jaka Čibej, Kaja Dobrovoljc, Tomaž Erjavec, Polona Gantar, Simon Krek, Tina Munda, Nejc Robida, Luka Terčon, Slavko Zitnik

Challenge:	a training corpus for linguistic annotation of modern standard Slovene has been in continuous development for 15 years.
Approach:	They introduce an upgrade of a training corpus for linguistic annotation of modern standard Slovene.
Outcome:	The revised corpus, built on its predecessor, doubles in size and depth of annotation layers.

Towards a Unified Taxonomy of Deep Syntactic Relations (2024.lrec-main)

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Kira Droganova, Daniel Zeman

Towards Universal Dependencies for Ancash Quechua (2024.lrec-main)

Challenge:	Currently, UD is the standard for morphology and surface syntax annotations, but it is only one step towards natural language understanding.
Approach:	They propose to use a set of universal semantic role labels for morphology and surface syntax in four Indo-European and one Uralic languages to analyze the data.
Outcome:	The proposed set of universal semantic role labels is based on the data from four Indo-European and one Uralic languages.

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Johanna Cordova

UCxn: Typologically-Informed Annotation of Constructions Atop Universal Dependencies (2024.lrec-main)

Challenge:	a new corpus of Quechua morphosyntactic features are described for Ancash Quechuan, the majority variety of the Central Quechual language family . the morphology of the language is a feature of the universal dependency (UD) schema . a syntactical parser would be the first NLP tool for a Quechuang language of this family based on the UD schema based upon the typology of the languages .
Approach:	They propose to describe some morphosyntactic features of Ancash Quechua . they propose to build a corpus annotated according to the universal dependency schema .
Outcome:	The proposed corpus is the first bilingual and sentence-aligned digital corpus in Ancash Quechua and Spanish.

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Leonie Weissweiler, Nina Böbel, Kirian Guiller, Santiago Herrera, Wesley Samuel Scivetti, Arthur Lorenzi, Nurit Melnik, Archna Bhatia, Hinrich Schütze, Lori Levin, Amir Zeldes, Joakim Nivre, William Croft, Nathan Schneider

Syntax as a Rosetta Stone: Universal Dependencies for In-Context Coptic Translation (2026.findings-acl)

Challenge:	Grammatical constructions that convey meaning through a particular combination of several morphosyntactic elements are not labeled holistically.
Approach:	They propose to augment UD annotations with a ‘UCxn’ annotation layer for such meaning-bearing grammatical constructions and to approach this in a typologically informed way so that morphosyntactic strategies can be compared across languages.
Outcome:	The proposed annotation layer could be used to annotate meaning-bearing constructions across languages and to compare them across languages.

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Abhishek Purushothama, Emma Thronson, Alexia Guo, Amir Zeldes

Machine Translation

Challenge:	Existing work using bilingual dictionaries to support inference for vocabulary items is lacking for low-resource languages.
Approach:	They propose to use universal dependency parses of input sentences to augment in-context learning prompts for low resource machine translation for the Coptic language.
Outcome:	The proposed approach achieves state-of-the-art results for the Coptic language.

Typology-Aware Multilingual Morphosyntactic Parsing with Joint Abstract Node Modeling (2026.acl-long)

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Kutay Acar, Gülşen Eryiğit