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For the low-resource languages, the standard benchmark corpora play an important role in the development of methods. In this article, we introduce two benchmark corpora with the largest sizes at sentence-level for two tasks: Aspect Category Detection and Aspect Polarity Classification in Vietnamese. Our corpora are annotated with high inter-annotator agreements for the restaurant and hotel domains. The release of our corpora would push forward the low-resource language processing community. In addition, we deploy and compare the effectiveness of supervised learning methods with a single and multi-task approach based on deep learning architectures. Experimental results on our corpora show that the multi-task approach based on BERT architecture outperforms the neural network architectures and the single approach. Our corpora and source code are published on this footnoted site.\n            <jats:sup>1<\/jats:sup>\n          <\/jats:p>","DOI":"10.1145\/3446678","type":"journal-article","created":{"date-parts":[[2021,5,26]],"date-time":"2021-05-26T14:10:16Z","timestamp":1622038216000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Two New Large Corpora for Vietnamese Aspect-based Sentiment Analysis at Sentence Level"],"prefix":"10.1145","volume":"20","author":[{"given":"Dang","family":"Van Thin","sequence":"first","affiliation":[{"name":"University of Information Technology, Vietnam National University, Ho Chi Minh City, VietNam"}]},{"given":"Ngan Luu-Thuy","family":"Nguyen","sequence":"additional","affiliation":[{"name":"University of Information Technology, Vietnam National University, Ho Chi Minh City, VietNam"}]},{"given":"Tri Minh","family":"Truong","sequence":"additional","affiliation":[{"name":"University of Information Technology, Vietnam National University, Ho Chi Minh City, VietNam"}]},{"given":"Lac Si","family":"Le","sequence":"additional","affiliation":[{"name":"University of Information Technology, Vietnam National University, Ho Chi Minh City, VietNam"}]},{"given":"Duy Tin","family":"Vo","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Lakehead University, Thunder Bay, ON P7B 5E1, Canada and VinAI Research, Ha Noi, VietNam"}]}],"member":"320","published-online":{"date-parts":[[2021,5,26]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/1611628.1611637"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.338"},{"key":"e_1_2_1_3_1","volume-title":"A quantitative and typological approach to correlating linguistic complexity. 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Aspect-based sentiment analysis using bert . In NEAL Proceedings of the 22nd Nordic Conference on Computional Linguistics (NoDaLiDa\u201919) . Link\u00f6ping University Electronic Press, Association for Computational Linguistics, Finland, 187\u2013196. Mickel Hoang, Oskar Alija Bihorac, and Jacobo Rouces. 2019. Aspect-based sentiment analysis using bert. In NEAL Proceedings of the 22nd Nordic Conference on Computional Linguistics (NoDaLiDa\u201919). Link\u00f6ping University Electronic Press, Association for Computational Linguistics, Finland, 187\u2013196."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cirpj.2018.06.003"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1080\/00207543.2016.1154208"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACOMP.2015.21"},{"key":"e_1_2_1_11_1","volume-title":"Proceedings of the 27th International Conference on Computational Linguistics. 925\u2013936","author":"Li Junjie","year":"2018","unstructured":"Junjie Li , Haitong Yang , and Chengqing Zong . 2018 . Document-level multi-aspect sentiment classification by jointly modeling users, aspects, and overall ratings . In Proceedings of the 27th International Conference on Computational Linguistics. 925\u2013936 . Junjie Li, Haitong Yang, and Chengqing Zong. 2018. Document-level multi-aspect sentiment classification by jointly modeling users, aspects, and overall ratings. 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