AdiBhashaa: A Community-Curated Benchmark for Machine Translation into Indian Tribal Languages

• URL: http://arxiv.org/abs/2512.04765v1
• Date: Thu, 04 Dec 2025 13:01:17 GMT
• Title: AdiBhashaa: A Community-Curated Benchmark for Machine Translation into Indian Tribal Languages
• Authors: Pooja Singh, Sandeep Kumar,
• Abstract summary: We present AdiBhashaa, a community-driven initiative that constructs the first open parallel corpora and baseline MT systems for four major Indian tribal languages.<n>This work combines participatory data creation with native speakers, human-in-the-loop validation, and systematic evaluation of both encoder-decoder MT models and large language models.
• Score: 3.2873201228433846
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models and multilingual machine translation (MT) systems increasingly drive access to information, yet many languages of the tribal communities remain effectively invisible in these technologies. This invisibility exacerbates existing structural inequities in education, governance, and digital participation. We present AdiBhashaa, a community-driven initiative that constructs the first open parallel corpora and baseline MT systems for four major Indian tribal languages-Bhili, Mundari, Gondi, and Santali. This work combines participatory data creation with native speakers, human-in-the-loop validation, and systematic evaluation of both encoder-decoder MT models and large language models. In addition to reporting technical findings, we articulate how AdiBhashaa illustrates a possible model for more equitable AI research: it centers local expertise, builds capacity among early-career researchers from marginalized communities, and foregrounds human validation in the development of language technologies.

Related papers

• Chitrarth: Bridging Vision and Language for a Billion People [4.434971952378384]
  We introduce Chitrarth (Chitra: Image; Artha: Meaning), an inclusive Vision-Language Model (VLM)<n>Our model effectively integrates a state-of-the-art (SOTA) multilingual Large Language Model (LLM) with a vision module, primarily trained on multilingual image-text data.<n>Our model achieves SOTA results for benchmarks across low resource languages while retaining its efficiency in English.
  arXiv  Detail & Related papers  (2025-02-21T11:38:40Z)
• Developing multilingual speech synthesis system for Ojibwe, Mi'kmaq, and Maliseet [4.889851090443267]
  We present lightweight flow matching multilingual text-to-speech (TTS) systems for Ojibwe, Mi'kmaq, and Maliseet, three Indigenous languages in North America.<n>Our results show that training a multilingual TTS model on three typologically similar languages can improve the performance over monolingual models.
  arXiv  Detail & Related papers  (2025-02-04T20:36:55Z)
• Harnessing the Power of Artificial Intelligence to Vitalize Endangered Indigenous Languages: Technologies and Experiences [31.62071644137294]
  We discuss the decreasing diversity of languages in the world and how working with Indigenous languages poses unique ethical challenges for AI and NLP. We report encouraging results in the development of high-quality machine learning translators for Indigenous languages. We present prototypes we have built in projects done in 2023 and 2024 with Indigenous communities in Brazil, aimed at facilitating writing.
  arXiv  Detail & Related papers  (2024-07-17T14:46:37Z)
• DIALIGHT: Lightweight Multilingual Development and Evaluation of Task-Oriented Dialogue Systems with Large Language Models [76.79929883963275]
  DIALIGHT is a toolkit for developing and evaluating multilingual Task-Oriented Dialogue (ToD) systems. It features a secure, user-friendly web interface for fine-grained human evaluation at both local utterance level and global dialogue level. Our evaluations reveal that while PLM fine-tuning leads to higher accuracy and coherence, LLM-based systems excel in producing diverse and likeable responses.
  arXiv  Detail & Related papers  (2024-01-04T11:27:48Z)
• Towards Bridging the Digital Language Divide [4.234367850767171]
  multilingual language processing systems often exhibit a hardwired, yet usually involuntary and hidden representational preference towards certain languages. We show that biased technology is often the result of research and development methodologies that do not do justice to the complexity of the languages being represented. We present a new initiative that aims at reducing linguistic bias through both technological design and methodology.
  arXiv  Detail & Related papers  (2023-07-25T10:53:20Z)
• 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)
• Building Machine Translation Systems for the Next Thousand Languages [102.24310122155073]
  We describe results in three research domains: building clean, web-mined datasets for 1500+ languages, developing practical MT models for under-served languages, and studying the limitations of evaluation metrics for these languages. We hope that our work provides useful insights to practitioners working towards building MT systems for currently understudied languages, and highlights research directions that can complement the weaknesses of massively multilingual models in data-sparse settings.
  arXiv  Detail & Related papers  (2022-05-09T00:24:13Z)
• A New Generation of Perspective API: Efficient Multilingual Character-level Transformers [66.9176610388952]
  We present the fundamentals behind the next version of the Perspective API from Google Jigsaw. At the heart of the approach is a single multilingual token-free Charformer model. We demonstrate that by forgoing static vocabularies, we gain flexibility across a variety of settings.
  arXiv  Detail & Related papers  (2022-02-22T20:55:31Z)
• Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition [54.92161571089808]
  Cross-lingual NER transfers knowledge from rich-resource language to languages with low resources. Existing cross-lingual NER methods do not make good use of rich unlabeled data in target languages. We develop a novel approach based on the ideas of semi-supervised learning and reinforcement learning.
  arXiv  Detail & Related papers  (2021-06-01T05:46:22Z)
• Crossing the Conversational Chasm: A Primer on Multilingual Task-Oriented Dialogue Systems [51.328224222640614]
  Current state-of-the-art ToD models based on large pretrained neural language models are data hungry. Data acquisition for ToD use cases is expensive and tedious.
  arXiv  Detail & Related papers  (2021-04-17T15:19:56Z)

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.