Curated Datasets and Neural Models for Machine Translation of Informal Registers between Mayan and Spanish Vernaculars

• URL: http://arxiv.org/abs/2404.07673v1
• Date: Thu, 11 Apr 2024 12:09:47 GMT
• Title: Curated Datasets and Neural Models for Machine Translation of Informal Registers between Mayan and Spanish Vernaculars
• Authors: Andrés Lou, Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez, Víctor M. Sánchez-Cartagena,
• Abstract summary: We develop, curate, and publicly release a set of corpora in several Mayan languages spoken in Guatemala and Southern Mexico. The datasets are parallel with Spanish, the dominant language of the region. We present neural machine translation models, trained on as many resources and Mayan languages as possible, and evaluated exclusively on our datasets.
• Score: 2.2061683015812026
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Mayan languages comprise a language family with an ancient history, millions of speakers, and immense cultural value, that, nevertheless, remains severely underrepresented in terms of resources and global exposure. In this paper we develop, curate, and publicly release a set of corpora in several Mayan languages spoken in Guatemala and Southern Mexico, which we call MayanV. The datasets are parallel with Spanish, the dominant language of the region, and are taken from official native sources focused on representing informal, day-to-day, and non-domain-specific language. As such, and according to our dialectometric analysis, they differ in register from most other available resources. Additionally, we present neural machine translation models, trained on as many resources and Mayan languages as possible, and evaluated exclusively on our datasets. We observe lexical divergences between the dialects of Spanish in our resources and the more widespread written standard of Spanish, and that resources other than the ones we present do not seem to improve translation performance, indicating that many such resources may not accurately capture common, real-life language usage. The MayanV dataset is available at https://github.com/transducens/mayanv.

Related papers

• Constructing and Expanding Low-Resource and Underrepresented Parallel Datasets for Indonesian Local Languages [0.0]
  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.
  arXiv  Detail & Related papers  (2024-04-01T09:24:06Z)
• 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)
• 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)
• Multi-lingual and Multi-cultural Figurative Language Understanding [69.47641938200817]
  Figurative language permeates human communication, but is relatively understudied in NLP. We create a dataset for seven diverse languages associated with a variety of cultures: Hindi, Indonesian, Javanese, Kannada, Sundanese, Swahili and Yoruba. Our dataset reveals that each language relies on cultural and regional concepts for figurative expressions, with the highest overlap between languages originating from the same region. All languages exhibit a significant deficiency compared to English, with variations in performance reflecting the availability of pre-training and fine-tuning data.
  arXiv  Detail & Related papers  (2023-05-25T15:30:31Z)
• Transfer to a Low-Resource Language via Close Relatives: The Case Study on Faroese [54.00582760714034]
  Cross-lingual NLP transfer can be improved by exploiting data and models of high-resource languages. We release a new web corpus of Faroese and Faroese datasets for named entity recognition (NER), semantic text similarity (STS) and new language models trained on all Scandinavian languages.
  arXiv  Detail & Related papers  (2023-04-18T08:42:38Z)
• Making a MIRACL: Multilingual Information Retrieval Across a Continuum of Languages [62.730361829175415]
  MIRACL is a multilingual dataset we have built for the WSDM 2023 Cup challenge. It focuses on ad hoc retrieval across 18 different languages. Our goal is to spur research that will improve retrieval across a continuum of languages.
  arXiv  Detail & Related papers  (2022-10-18T16:47:18Z)
• 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)
• The first large scale collection of diverse Hausa language datasets [0.0]
  Hausa is considered well-studied and documented language among the sub-Saharan African languages. It is estimated that over 100 million people speak the language. We provide an expansive collection of curated datasets consisting of both formal and informal forms of the language.
  arXiv  Detail & Related papers  (2021-02-13T19:34:20Z)

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.