Constructing and Expanding Low-Resource and Underrepresented Parallel Datasets for Indonesian Local Languages

• URL: http://arxiv.org/abs/2404.01009v1
• Date: Mon, 1 Apr 2024 09:24:06 GMT
• Title: Constructing and Expanding Low-Resource and Underrepresented Parallel Datasets for Indonesian Local Languages
• Authors: Joanito Agili Lopo, Radius Tanone,
• Abstract summary: We introduce Bhinneka Korpus, a multilingual parallel corpus featuring five Indonesian local languages. Our goal is to enhance access and utilization of these resources, extending their reach within the country.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In Indonesia, local languages play an integral role in the culture. However, the available Indonesian language resources still fall into the category of limited data in the Natural Language Processing (NLP) field. This is become problematic when build NLP model for these languages. To address this gap, we introduce Bhinneka Korpus, a multilingual parallel corpus featuring five Indonesian local languages. Our goal is to enhance access and utilization of these resources, extending their reach within the country. We explained in a detail the dataset collection process and associated challenges. Additionally, we experimented with translation task using the IBM Model 1 due to data constraints. The result showed that the performance of each language already shows good indications for further development. Challenges such as lexical variation, smoothing effects, and cross-linguistic variability are discussed. We intend to evaluate the corpus using advanced NLP techniques for low-resource languages, paving the way for multilingual translation models.

Related papers

• Natural Language Processing for Dialects of a Language: A Survey [56.93337350526933]
  State-of-the-art natural language processing (NLP) models are trained on massive training corpora, and report a superlative performance on evaluation datasets. This survey delves into an important attribute of these datasets: the dialect of a language. Motivated by the performance degradation of NLP models for dialectic datasets and its implications for the equity of language technologies, we survey past research in NLP for dialects in terms of datasets, and approaches.
  arXiv  Detail & Related papers  (2024-01-11T03:04:38Z)
• NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages [54.808217147579036]
  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.
  arXiv  Detail & Related papers  (2023-09-19T14:42:33Z)
• Improving Natural Language Inference in Arabic using Transformer Models and Linguistically Informed Pre-Training [0.34998703934432673]
  This paper addresses the classification of Arabic text data in the field of Natural Language Processing (NLP) To overcome this limitation, we create a dedicated data set from publicly available resources. We find that a language-specific model (AraBERT) performs competitively with state-of-the-art multilingual approaches.
  arXiv  Detail & Related papers  (2023-07-27T07:40:11Z)
• Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction [102.13536517783837]
  Most languages from the Americas are among them, having a limited amount of parallel and monolingual data, if any. We discuss the recent advances and findings and open questions, product of an increased interest of the NLP community in these languages.
  arXiv  Detail & Related papers  (2023-06-11T23:27:47Z)
• NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages [100.59889279607432]
  We focus on developing resources for languages in Indonesia. 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.
  arXiv  Detail & Related papers  (2022-05-31T17:03:50Z)
• Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios? [0.0]
  We focus on North-African colloquial dialectal Arabic written using an extension of the Latin script, called NArabizi. We show that a character-based model trained on only 99k sentences of NArabizi and fined-tuned on a small treebank leads to performance close to those obtained with the same architecture pre-trained on large multilingual and monolingual models.
  arXiv  Detail & Related papers  (2021-10-26T14:59:16Z)
• LaoPLM: Pre-trained Language Models for Lao [3.2146309563776416]
  Pre-trained language models (PLMs) can capture different levels of concepts in context and hence generate universal language representations. Although PTMs have been widely used in most NLP applications, it is under-represented in Lao NLP research. We construct a text classification dataset to alleviate the resource-scare situation of the Lao language. We present the first transformer-based PTMs for Lao with four versions: BERT-small, BERT-base, ELECTRA-small and ELECTRA-base, and evaluate it over two downstream tasks: part-of-speech tagging and text classification.
  arXiv  Detail & Related papers  (2021-10-12T11:13:07Z)
• Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translation [53.22775597051498]
  We present a continual pre-training framework on mBART to effectively adapt it to unseen languages. Results show that our method can consistently improve the fine-tuning performance upon the mBART baseline. Our approach also boosts the performance on translation pairs where both languages are seen in the original mBART's pre-training.
  arXiv  Detail & Related papers  (2021-05-09T14:49:07Z)
• UNKs Everywhere: Adapting Multilingual Language Models to New Scripts [103.79021395138423]
  Massively multilingual language models such as multilingual BERT (mBERT) and XLM-R offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks. Due to their limited capacity and large differences in pretraining data, there is a profound performance gap between resource-rich and resource-poor target languages. We propose novel data-efficient methods that enable quick and effective adaptation of pretrained multilingual models to such low-resource languages and unseen scripts.
  arXiv  Detail & Related papers  (2020-12-31T11:37:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.