Papers › NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely...

NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages

19 Sep 2023arXiv:2309.10661archive 2025-07-28

Samuel Cahyawijaya, Holy Lovenia, Fajri Koto, Dea Adhista, Emmanuel Dave, Sarah Oktavianti, Salsabil Maulana Akbar, Jhonson Lee, Nuur Shadieq, Tjeng Wawan Cenggoro, Hanung Wahyuning Linuwih, Bryan Wilie, Galih Pradipta Muridan, Genta Indra Winata, David Moeljadi, Alham Fikri Aji, Ayu Purwarianti, Pascale Fung

Democratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document translation. While these methods have proven effective and cost-efficient, we have identified limitations in the resulting corpora, including a lack of lexical diversity and cultural relevance to local communities. To address this gap, we conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content. In addition, we present the \datasetname{} benchmark, encompassing 12 underrepresented and extremely low-resource languages spoken by millions of individuals in Indonesia. Our empirical experiment results using existing multilingual large language models conclude the need to extend these models to more underrepresented languages. We release the NusaWrites dataset at https://github.com/IndoNLP/nusa-writes.

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create_output_directory indonlp/nusa-writes/main_generation_classic.py official repository ran Apache-2.0 (permissive) · 19a1892a06e4be89 · report
evaluate_classical indonlp/nusa-writes/main_generation_classic.py official repository ran Apache-2.0 (permissive) · 38ca8eb2c0964a43 · report
evaluate_language_model indonlp/nusa-writes/main_generation.py official repository ran Apache-2.0 (permissive) · daa8d47d0f9da670 · report
get_lr indonlp/nusa-writes/main_generation.py official repository ran · honoured contract Apache-2.0 (permissive) · e9c7d263fd12f86d · report
hyperparam_tuning indonlp/nusa-writes/boomer/main_boomer.py official repository ran Apache-2.0 (permissive) · 80e1967386ea09db · report
metrics_to_string indonlp/nusa-writes/main_generation.py official repository ran · our draft was wrong Apache-2.0 (permissive) · 2c8f98e551054b67 · report
to_prompt indonlp/nusa-writes/main_nlg_prompt.py official repository ran Apache-2.0 (permissive) · d9aa425b275a90da · report
to_prompt indonlp/nusa-writes/main_nlu_prompt.py official repository ran Apache-2.0 (permissive) · d84b67034badaff8 · report
translate_one_sentence_panlex indonlp/nusa-writes/main_generation_classic.py official repository ran Apache-2.0 (permissive) · eab6642a577f3db1 · report
get_logprobs indonlp/nusa-writes/main_nlu_prompt.py official repository unverified Apache-2.0 (permissive) · dc1ba6a2eff33540 · report
load_data indonlp/nusa-writes/boomer/main_boomer.py official repository unverified Apache-2.0 (permissive) · 97cce8a19961338b · report
predict_classification indonlp/nusa-writes/main_nlu_prompt.py official repository unverified Apache-2.0 (permissive) · c9ff0f13e10f8152 · report
predict_generation indonlp/nusa-writes/main_nlg_prompt.py official repository unverified Apache-2.0 (permissive) · 4554d73b7e0f9471 · report
train_and_test indonlp/nusa-writes/boomer/main_boomer.py official repository unverified Apache-2.0 (permissive) · 1ee1d7db8057684b · report

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