Challenge: Indigenous languages of the American continent are highly diverse, but have received little attention from the technological perspective.
Approach: They review the research, the digital resources and the available NLP systems for indigenous languages of the American continent . they stress the need of developing language resources and NLP tools for these languages .
Outcome: The authors review the research and the available NLP systems on indigenous languages of the Americas . they argue that the lack of resources and tools can have a negative impact on the communities which depend on these languages .

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NLP Progress in Indigenous Latin American Languages (2024.naacl-long)

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Challenge: a new study examines the marginalization of indigenous languages in the face of rapid technological advancements.
Approach: They highlight the cultural richness of indigenous languages and the risk they face of being overlooked in the realm of natural language processing.
Outcome: The authors highlight the cultural richness of indigenous languages and their risk of being overlooked in the realm of natural language processing.
Challenges and Strategies in Cross-Cultural NLP (2022.acl-long)

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Challenge: Various efforts have been made to accommodate linguistic diversity and serve speakers of many different languages.
Approach: They propose a framework to examine cultural differences in NLP to better serve users . they argue that cultural knowledge, preferences and values can affect NLP practices .
Outcome: The proposed framework examines how cultural knowledge, preferences and values can affect NLP practices.
Indigenous language technologies in Canada: Assessment, challenges, and successes (C18-1)

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Challenge: There are approximately 60 Indigenous languages currently spoken in Canada.
Approach: They examine which technologies have been developed and which are feasible to develop for the 60 Indigenous languages spoken in Canada.
Outcome: The proposed technologies are based on the existing technologies and are feasible for most or all of these languages.
One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia (2022.acl-long)

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Challenge: There are more than 700 languages spoken in Indonesia, equal to 10% of the world's languages, second only to Papua New Guinea.
Approach: They focus on the languages spoken in Indonesia, the world's second most linguistically diverse nation, and the fourth most populous nation of the world.
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Indigenous Languages Spoken in Argentina: A Survey of NLP and Speech Resources (2025.coling-main)

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Challenge: Currently, no unified information on speakers and computational tools are available for these languages.
Approach: They present a systematization of the indigenous languages spoken in Argentina, along with national demographic data on the country’s Indigenous population.
Outcome: The proposed systematization of the indigenous languages spoken in Argentina, along with national demographic data on the country’s Indigenous population, is based on the Argentine population.
Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead (2025.emnlp-main)

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Challenge: African languages are often left behind in state-of-the-art natural language processing systems and large language models.
Approach: They analyze 884 research papers on NLP for African languages published over past five years . they identify key trends shaping the field and outline promising directions .
Outcome: The findings identify key trends shaping the field and outline promising directions . the authors analyze 884 research papers on NLP for African languages published over the past five years .
Not always about you: Prioritizing community needs when developing endangered language technology (2022.acl-long)

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Challenge: low-resource languages lack the quantity of data needed to train statistical and machine learning tools and models.
Approach: They propose to use language technology to support endangered languages' revitalization . they propose to work with indigenous speakers to develop technology for such training .
Outcome: The authors discuss the challenges that researchers and indigenous speech community members face when working together to develop language technology to support endangered languages.
The State and Fate of Linguistic Diversity and Inclusion in the NLP World (2020.acl-main)

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Challenge: a small number of the over 7000 languages of the world are represented in the rapidly evolving language technologies and applications.
Approach: They examine the relationship between types of languages, resources, and their representation in NLP conferences to understand the trajectory that different languages have followed over time.
Outcome: The proposed model will help to bridge the gap between languages and their resources and convince the ACL community to prioritise the resolution of the predicaments highlighted.
What a Creole Wants, What a Creole Needs (2022.lrec-1)

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Challenge: Recent efforts to improve the quality of high-resource languages focus on translating existing datasets into other languages, but this approach ignores that different language communities have different needs.
Approach: They examine how things needed from language technology can change dramatically from one language to another.
Outcome: The proposed method ignores that different language communities have different needs.
Towards Afrocentric NLP for African Languages: Where We Are and Where We Can Go (2022.acl-long)

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Challenge: ACL 2022 special Theme on "Language Diversity: from Low Resource to Endangered Languages" focuses on linguistic and sociopolitical challenges facing development of NLP technologies for African languages .
Approach: They propose a typological framework for linguistic and sociopolitical challenges for NLP in African languages.
Outcome: The main objective of this study is to motivate and advocate for an Afrocentric approach to technology development.

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