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NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages

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arxiv 2205.15960 v2 pith:BQ2JRT2G submitted 2022-05-31 cs.CL

Genta Indra Winata , Alham Fikri Aji , Samuel Cahyawijaya , Rahmad Mahendra , Fajri Koto , Ade Romadhony , Kemal Kurniawan , David Moeljadi
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Radityo Eko Prasojo Pascale Fung Timothy Baldwin Jey Han Lau Rico Sennrich Sebastian Ruder
This is my paper · ORCID
classification cs.CL
keywords languagesindonesiaparallelresourcesdatasetdespiteindonesianresource
verification ladder T0 review T1 audit T2 compute T3 formal
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Natural language processing (NLP) has a significant impact on society via technologies such as machine translation and search engines. Despite its success, NLP technology is only widely available for high-resource languages such as English and Chinese, while it remains inaccessible to many languages due to the unavailability of data resources and benchmarks. In this work, we focus on developing resources for languages in Indonesia. Despite being the second most linguistically diverse country, most languages in Indonesia are categorized as endangered and some are even extinct. We develop the first-ever parallel resource for 10 low-resource languages in Indonesia. Our resource includes datasets, a multi-task benchmark, and lexicons, as well as a parallel Indonesian-English dataset. We provide extensive analyses and describe the challenges when creating such resources. We hope that our work can spark NLP research on Indonesian and other underrepresented languages.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Improving Bangla Linguistics: Advanced LSTM, Bi-LSTM, and Seq2Seq Models for Translating Sylheti to Modern Bangla

    cs.CL 2025-05 reject novelty 4.0 of 10

    The authors report that an LSTM model achieves 89.3% accuracy on a 1,200-sentence Sylheti-to-Modern Bangla translation task, but the evaluation protocol and dataset are not described rigorously enough to support the claim.

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