@inproceedings{joshi-etal-2025-elr,
title = "{ELR}-1000: A Community-Generated Dataset for Endangered {I}ndic Indigenous Languages",
author = "Joshi, Neha and
Gogoi, Pamir and
Mirza, AasimBaig and
Jansari, Aayush and
Yadavalli, Aditya and
Pandey, Ayushi and
Shukla, Arunima and
Sudharsan, Deepthi and
Bali, Kalika and
Seshadri, Vivek",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-long.131/",
pages = "2441--2457",
ISBN = "979-8-89176-298-5",
abstract = "We present a culturally-grounded multimodal dataset of 1,060 traditional recipes crowdsourced from rural communities across remote regions of Eastern India, spanning 10 endangered languages. These recipes, rich in linguistic and cultural nuance, were collected using a mobile interface designed for contributors with low digital literacy. Endangered Language Recipes (ELR)-1000{---}captures not only culinary practices but also the socio-cultural context embedded in indigenous food traditions. We evaluate the performance of several state-of-the-art large language models (LLMs) on translating these recipes into English and find the following: despite the models' capabilities, they struggle with low-resource, culturally-specific language. However, we observe that providing targeted context{---}including background information about the languages, translation examples, and guidelines for cultural preservation{---}leads to significant improvements in translation quality. Our results underscore the need for benchmarks that cater to underrepresented languages and domains to advance equitable and culturally-aware language technologies. As part of this work, we release the ELR-1000 dataset to the NLP community, hoping it motivates the development of language technologies for endangered languages."
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<abstract>We present a culturally-grounded multimodal dataset of 1,060 traditional recipes crowdsourced from rural communities across remote regions of Eastern India, spanning 10 endangered languages. These recipes, rich in linguistic and cultural nuance, were collected using a mobile interface designed for contributors with low digital literacy. Endangered Language Recipes (ELR)-1000—captures not only culinary practices but also the socio-cultural context embedded in indigenous food traditions. We evaluate the performance of several state-of-the-art large language models (LLMs) on translating these recipes into English and find the following: despite the models’ capabilities, they struggle with low-resource, culturally-specific language. However, we observe that providing targeted context—including background information about the languages, translation examples, and guidelines for cultural preservation—leads to significant improvements in translation quality. Our results underscore the need for benchmarks that cater to underrepresented languages and domains to advance equitable and culturally-aware language technologies. As part of this work, we release the ELR-1000 dataset to the NLP community, hoping it motivates the development of language technologies for endangered languages.</abstract>
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%0 Conference Proceedings
%T ELR-1000: A Community-Generated Dataset for Endangered Indic Indigenous Languages
%A Joshi, Neha
%A Gogoi, Pamir
%A Mirza, AasimBaig
%A Jansari, Aayush
%A Yadavalli, Aditya
%A Pandey, Ayushi
%A Shukla, Arunima
%A Sudharsan, Deepthi
%A Bali, Kalika
%A Seshadri, Vivek
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-298-5
%F joshi-etal-2025-elr
%X We present a culturally-grounded multimodal dataset of 1,060 traditional recipes crowdsourced from rural communities across remote regions of Eastern India, spanning 10 endangered languages. These recipes, rich in linguistic and cultural nuance, were collected using a mobile interface designed for contributors with low digital literacy. Endangered Language Recipes (ELR)-1000—captures not only culinary practices but also the socio-cultural context embedded in indigenous food traditions. We evaluate the performance of several state-of-the-art large language models (LLMs) on translating these recipes into English and find the following: despite the models’ capabilities, they struggle with low-resource, culturally-specific language. However, we observe that providing targeted context—including background information about the languages, translation examples, and guidelines for cultural preservation—leads to significant improvements in translation quality. Our results underscore the need for benchmarks that cater to underrepresented languages and domains to advance equitable and culturally-aware language technologies. As part of this work, we release the ELR-1000 dataset to the NLP community, hoping it motivates the development of language technologies for endangered languages.
%U https://aclanthology.org/2025.ijcnlp-long.131/
%P 2441-2457
Markdown (Informal)
[ELR-1000: A Community-Generated Dataset for Endangered Indic Indigenous Languages](https://aclanthology.org/2025.ijcnlp-long.131/) (Joshi et al., IJCNLP-AACL 2025)
ACL
- Neha Joshi, Pamir Gogoi, AasimBaig Mirza, Aayush Jansari, Aditya Yadavalli, Ayushi Pandey, Arunima Shukla, Deepthi Sudharsan, Kalika Bali, and Vivek Seshadri. 2025. ELR-1000: A Community-Generated Dataset for Endangered Indic Indigenous Languages. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 2441–2457, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.