This work presents a native QA dataset for an East African language, Tigrinya. However, these advances focus on high-resourced languages such as English, while the task remains unexplored for most other languages, mainly due to the lack of annotated datasets. Question-Answering (QA) has seen significant advances recently, achieving near human-level performance over some benchmarks. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)Īssociation for Computational Linguistics

Question-Answering in a Low-resourced Language: Benchmark Dataset and Models for Tigrinya We make the dataset, models, and leaderboard publicly available.', Cite (Informal): Question-Answering in a Low-resourced Language: Benchmark Dataset and Models for Tigrinya (Gaim et al., ACL 2023) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: Video: = 'Question-Answering in a Low-resourced Language: Benchmark Dataset and Models for, indicating that the benchmark presents a good challenge for future work. Association for Computational Linguistics. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11857–11870, Toronto, Canada. Question-Answering in a Low-resourced Language: Benchmark Dataset and Models for Tigrinya. Naoaki Okazaki Venue: ACL SIG: Publisher: Association for Computational Linguistics Note: Pages: 11857–11870 Language: URL: DOI: 10.18653/v1/2023.acl-long.661 Bibkey: gaim-etal-2023-question Cite (ACL): Fitsum Gaim, Wonsuk Yang, Hancheol Park, and Jong Park. Anthology ID: 2023.acl-long.661 Volume: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) Month: July Year: 2023 Address: Toronto, Canada Editors: Anna Rogers, We make the dataset, models, and leaderboard publicly available. Our strong baseline models reach 76% in the F1 score, while the estimated human performance is 92%, indicating that the benchmark presents a good challenge for future work. We present comprehensive experiments and analyses of several resource-efficient approaches to QA, including monolingual, cross-lingual, and multilingual setups, along with comparisons against machine-translated silver data.

The dataset construction method is discussed, which is applicable to constructing similar resources for related languages. The dataset contains 10.6K question-answer pairs spanning 572 paragraphs extracted from 290 news articles on various topics. Abstract Question-Answering (QA) has seen significant advances recently, achieving near human-level performance over some benchmarks.

Benchmark in construction site