Domain Generalization in Vietnamese Dependency Parsing: A Novel Benchmark and Domain Gap Analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AZ3IVR7F7" target="_blank" >RIV/00216208:11320/26:Z3IVR7F7 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-981-96-4282-3_14" target="_blank" >http://dx.doi.org/10.1007/978-981-96-4282-3_14</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-96-4282-3_14" target="_blank" >10.1007/978-981-96-4282-3_14</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Domain Generalization in Vietnamese Dependency Parsing: A Novel Benchmark and Domain Gap Analysis

Popis výsledku v původním jazyce

Dependency parsing has received significant attention from the research community due to its recognized applications across diverse areas of natural language processing (NLP). However, the majority of dependency parsing studies to date have not addressed the out-of-domain problem, where the data in the testing phase are in a different distribution compared with data in training domains, despite this being a common problem in practice. Furthermore, Vietnamese is still considered a low-resource language in parsing tasks, as most standard treebanks are primarily developed for more widely spoken languages such as English and Chinese. This shortage pushes the difficulty of studies of Vietnamese dependency parsing task even further. To advance research on domain generalization in Vietnamese dependency parsing task, this paper introduces a new treebank called DGDT(VietnameseDomainGeneralizationDependencyTreebank), where domains in train/dev/test set are completely separated. This is the distinction of our treebank, compared to other Vietnamese dependency treebanks. We also release DGDTMark, a cross-domain Vietnamese dependency parsing benchmark suite using our treebank to assess the generalization ability of parsers over domains. Moreover, our suite can support further research in analyzing the impacts of domain gaps on the dependency parsing task. Through experiments, we observe that the performance of parsers is most affected by two gaps: newspaper topics and writing styles. Besides, the performance drops remarkably by 3.27% UAS and 5.09% LAS in the scenario with the largest domain gap, which proves that our treebank poses a significant challenge for further research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Název v anglickém jazyce

Domain Generalization in Vietnamese Dependency Parsing: A Novel Benchmark and Domain Gap Analysis

Popis výsledku anglicky

Dependency parsing has received significant attention from the research community due to its recognized applications across diverse areas of natural language processing (NLP). However, the majority of dependency parsing studies to date have not addressed the out-of-domain problem, where the data in the testing phase are in a different distribution compared with data in training domains, despite this being a common problem in practice. Furthermore, Vietnamese is still considered a low-resource language in parsing tasks, as most standard treebanks are primarily developed for more widely spoken languages such as English and Chinese. This shortage pushes the difficulty of studies of Vietnamese dependency parsing task even further. To advance research on domain generalization in Vietnamese dependency parsing task, this paper introduces a new treebank called DGDT(VietnameseDomainGeneralizationDependencyTreebank), where domains in train/dev/test set are completely separated. This is the distinction of our treebank, compared to other Vietnamese dependency treebanks. We also release DGDTMark, a cross-domain Vietnamese dependency parsing benchmark suite using our treebank to assess the generalization ability of parsers over domains. Moreover, our suite can support further research in analyzing the impacts of domain gaps on the dependency parsing task. Through experiments, we observe that the performance of parsers is most affected by two gaps: newspaper topics and writing styles. Besides, the performance drops remarkably by 3.27% UAS and 5.09% LAS in the scenario with the largest domain gap, which proves that our treebank poses a significant challenge for further research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Commun. Comput. Info. Sci.

ISBN

978-981-96-4281-6

ISSN

—

e-ISSN

—

Počet stran výsledku

15

Strana od-do

167-181

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

—

Místo konání akce

Danang

Datum konání akce

1. 1. 2026

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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