Month: January 2025

Some Considerations for the Preservation of Endangered Languages Using Low-Resource Machine Translation

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Abstract

While widespread languages remain actively prevalent in digital mediums, endangered languages such as Indigenous Australian languages, are often scarce in textual resources and lack a substantial digital presence. This diminishes the survivability of their language and cultural heritages, making language preservation initiatives important. Neural Machine Translation (NMT) efforts for low-resource languages can help accelerate the digitisation and preservation of such languages by further enabling translation, information access, and second-language acquisition. This study explores the challenges and considerations with low-resource machine translation (MT), specifically focusing on primarily oral Indigenous Australian languages with minimally available textual resources. Additionally, we explore the existing challenges in the search for quality data and ethical research considerations in approaching Indigenous Cultural Intellectual Property (ICIP). As NMT performance often scales with the quality and quantity of multilingual corpora, we explore promising alternatives such as leveraging large language models (LLMs) to tackle severely low-resource MT as a few-shot prompting translation task. By employing a data imputation approach inspired by Continuous Bag-of-Words (CBOW) to strengthen a prompt’s contextual relevancy, we enhance translations generated by LLMs, achieving a chrF score of 37.3 on imputed data, compared to a baseline of 31.6 with GPT-3.5, and 39.3 compared to a baseline of 38.3 on GPT-4. Through our work, we hope to establish a foundation for future efforts in preserving Indigenous Australian languages.

Some Considerations for the Preservation of Endangered Languages Using Low-Resource Machine Translation

Latest Results

Abstract

While widespread languages remain actively prevalent in digital mediums, endangered languages such as Indigenous Australian languages, are often scarce in textual resources and lack a substantial digital presence. This diminishes the survivability of their language and cultural heritages, making language preservation initiatives important. Neural Machine Translation (NMT) efforts for low-resource languages can help accelerate the digitisation and preservation of such languages by further enabling translation, information access, and second-language acquisition. This study explores the challenges and considerations with low-resource machine translation (MT), specifically focusing on primarily oral Indigenous Australian languages with minimally available textual resources. Additionally, we explore the existing challenges in the search for quality data and ethical research considerations in approaching Indigenous Cultural Intellectual Property (ICIP). As NMT performance often scales with the quality and quantity of multilingual corpora, we explore promising alternatives such as leveraging large language models (LLMs) to tackle severely low-resource MT as a few-shot prompting translation task. By employing a data imputation approach inspired by Continuous Bag-of-Words (CBOW) to strengthen a prompt’s contextual relevancy, we enhance translations generated by LLMs, achieving a chrF score of 37.3 on imputed data, compared to a baseline of 31.6 with GPT-3.5, and 39.3 compared to a baseline of 38.3 on GPT-4. Through our work, we hope to establish a foundation for future efforts in preserving Indigenous Australian languages.

Empowering Agency: Enhancing Health Literacy Among Migrant Women Through Health Parties: A Case Study

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Abstract

This chapter explores how agency involvement through Health Parties can generate and promote learning agencies for migrant women to enforce health literacy. The Norwegian healthcare system struggles to provide sufficient responses to the needs of current and potential patients of the migrant population, particularly migrant women. A lack of cultural sensitivity and a more bottom-up approach is needed. Inspired by Tupperware Parties, this chapter explores the model of Health Parties, initiated by the female network of migrant women, called Kvinnenettverket Noor in Norwegian. A Health Party is based on a model where a host invites female friends, family, and acquaintances to a party to share information and learn about a relevant health issue by creating an appropriate space for discussion among experts and participants. This model can be employed when working to fulfil the United Nations Sustainable Development Goals concerning health and equity for migrant women. An explorative qualitative community-based participatory study design was employed. Data were collected by analysing participant observations and semi-structured face-to-face interviews in seven arranged Health Parties from September 2015 to March 2016. Health Parties generate learning agencies by providing space for active participation for migrant women. The healthcare system, which is based on Norwegian norms and culture, created cultural alienation and barriers that migrant women need space and knowledge to overcome. The results show the need for a public healthcare system and its professionals to be more sensitive and better adjusted to cultural diversity. They also provide insights into how migrant women gain agency about health issues by attending a Health Party. New ways of communication are required and found in the model of Health Parties for addressing health literacy among migrant women.

The Geopolitics of Food Security

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Abstract

A spate of accelerating global food crises over the past 15 years has boosted the profile of food security as a site of world politics. Long a matter of economic concern, food security has become a subject for geopolitical calculation and strategy, albeit in ways that tangle with and reflect back on global economic markets, supply chains, and trade. This chapter outlines a geopolitical economy approach to understanding this shift, blending insights from international political economy with those from critical geopolitics to attend to both the material and discursive dimensions of contemporary global hunger. The chapter applies the geopolitical economy approach to understanding three recent global food crises associated with the 2007/2008 spike in food prices, the COVID-19 pandemic, and Russia’s war in Ukraine from 2022. Highlighting the diverse forms of political and economic power that bisect and shape global food security, the chapter engages recent debates around topics including agricultural self-sufficiency, the weaponization and securitization of food, food protectionism, and food diplomacy along the way.