Myanmar XNLI: building a dataset and exploring low-resource approaches to natural language inference with Myanmar.
Despite dramatic recent progress in NLP, it is still a major challenge to apply Large Language Models (LLM) to low-resource languages. This is made visible in benchmarks such as Cross-Lingual Natural Language Inference (XNLI), a key task that demonstrates cross-lingual capabilities of NLP systems ac...
| Publicado en: | Language Resources & Evaluation Vol. 59; no. 3; pp. 3267 - 3311 |
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| Autores principales: | , |
| Formato: | Artículo |
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Springer Nature
Sep2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=186909097&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 186909097 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Sep2025 vid: 59 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 186909097 10.1007/s10579-025-09844-1 ppf: 3267 ppct: 44 formats: fmt: – @attributes: type: T – @attributes: type: P size: 2.5MB tig: atl: Myanmar XNLI: building a dataset and exploring low-resource approaches to natural language inference with Myanmar. aug: au: Htet, Aung Kyaw Dras, Mark affil: https://ror.org/01sf06y89 Department of Computing, Macquarie University, Sydney, Australia su: Low-resource languages Data augmentation Acquisition of data Citizen science Natural languages Natural language processing Language models Myanmar sug: subj: Myanmar Low-resource languages Data augmentation Acquisition of data Citizen science Natural languages Natural language processing Language models keyword: Burmese Communication and Culture Linguistics Information and Computing Sciences Artificial Intelligence and Image Processing Language Low-resource Natural language inference ab: Despite dramatic recent progress in NLP, it is still a major challenge to apply Large Language Models (LLM) to low-resource languages. This is made visible in benchmarks such as Cross-Lingual Natural Language Inference (XNLI), a key task that demonstrates cross-lingual capabilities of NLP systems across a set of 15 languages. In this paper, we extend the XNLI task for one additional low-resource language, Myanmar, as a proxy challenge for broader low-resource languages, and make three core contributions. First, we build a dataset called Myanmar XNLI (myXNLI) using community crowd-sourced methods, as an extension to the existing XNLI corpus. This involves a two-stage process of community-based construction followed by expert verification; through an analysis, we demonstrate and quantify the value of the expert verification stage in the context of community-based construction for low-resource languages. We make the myXNLI dataset available to the community for future research. Second, we carry out evaluations of recent multilingual language models on the myXNLI benchmark, as well as explore data-augmentation methods to improve model performance. Our data-augmentation methods improve model accuracy by up to 2 percentage points for Myanmar, while uplifting other languages at the same time. Third, we investigate how well these data-augmentation methods generalise to other low-resource languages in the XNLI dataset. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2025. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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