LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models | Paper Citation Record

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This paper cites Common Voice: A Massively-Multilingual Speech Corpus.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Common Voice: A Massively-Multilingual Speech Corpus

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This paper cites Introducing Syllable Tokenization for Low-resource Languages: A Case Study with Swahili.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Introducing Syllable Tokenization for Low-resource Languages: A Case Study with Swahili

Reference 2

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This paper cites Low-Resource Speech-to-Text Translation.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Low-Resource Speech-to-Text Translation

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This paper cites Language Model Prior for Low-Resource Neural Machine Translation.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Language Model Prior for Low-Resource Neural Machine Translation

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This paper cites Decolonising speech and language technology.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Decolonising speech and language technology

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This paper cites Cross-Lingual Morphological Tagging for Low-Resource Languages.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Cross-Lingual Morphological Tagging for Low-Resource Languages

Reference 6

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This paper cites LLMs Are Few-Shot In-Context Low-Resource Language Learners.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models LLMs Are Few-Shot In-Context Low-Resource Language Learners

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This paper cites A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages

Reference 8

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This paper cites Two decades of unsupervised pos induction: How far have we come? In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pages 575–584, 2010.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Two decades of unsupervised pos induction: How far have we come? In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pages 575–584, 2010

Reference 9

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This paper cites Selection criteria for low resource language programs.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Selection criteria for low resource language programs

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This paper cites Data collection pipeline for low-resource languages: A case study on constructing a tetun text corpus.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Data collection pipeline for low-resource languages: A case study on constructing a tetun text corpus

Reference 11

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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This paper cites Statistical identification of language.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Statistical identification of language

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This paper cites What can we get from 1000 tokens? a case study of multilingual pos tagging for resource-poor languages.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models What can we get from 1000 tokens? a case study of multilingual pos tagging for resource-poor languages

Reference 14

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This paper cites Learning when to trust distant supervision: An application to low-resource POS tagging using cross-lingual projection.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Learning when to trust distant supervision: An application to low-resource POS tagging using cross-lingual projection

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This paper cites Model Transfer for Tagging Low-resource Languages using a Bilingual Dictionary.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Model Transfer for Tagging Low-resource Languages using a Bilingual Dictionary

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This paper cites Manuale di linguistica sarda.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Manuale di linguistica sarda

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This paper cites Ideologies and expressed attitudes in internet: Comparing ethnic identities in two regional communities (veneto and sardinia).

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Ideologies and expressed attitudes in internet: Comparing ethnic identities in two regional communities (veneto and sardinia)

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This paper cites Metrics for Multi-Class Classification: an Overview.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Metrics for Multi-Class Classification: an Overview

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LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Speech generation for indigenous language education

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This paper cites Recent advances in apertium, a free/open-source rule-based machine translation platform for low-resource languages.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Recent advances in apertium, a free/open-source rule-based machine translation platform for low-resource languages

Reference 21

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This paper cites Practical Natural Language Processing for Low-Resource Languages.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Practical Natural Language Processing for Low-Resource Languages

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LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Synthesizer voice quality of new languages calibrated with mean mel cepstral distortion

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This paper cites An augmented translation technique for low resource language pair: Sanskrit to hindi translation.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models An augmented translation technique for low resource language pair: Sanskrit to hindi translation

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This paper cites adaptmllm: Fine-tuning multilingual language models on low-resource languages with integrated llm playgrounds.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models adaptmllm: Fine-tuning multilingual language models on low-resource languages with integrated llm playgrounds

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This paper cites A survey on evaluation metrics for machine translation.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models A survey on evaluation metrics for machine translation

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LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Unresolved cited work

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LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Practical Comparable Data Collection for Low-Resource Languages via Images

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LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Low-resource Languages: A Review of Past Work and Future Challenges

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LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Neural transition-based string transduction for limited-resource setting in morphology

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LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Lingue di minoranza a scuola: uno sguardoalla sardegna a dieci anni dalla legge 482/99

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LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Dialetti su instagram: usi, differenze e atteggiamenti linguistici

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This paper cites The internet as a rescue tool of endangered languages: Sardinian.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models The internet as a rescue tool of endangered languages: Sardinian

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LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Class-based n-gram models of natural language

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LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Efforts to standardise minority languages

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LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Merlo and Scuola normale superiore (Italy)

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LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Applying AI-based Tools and Technologies Towards Revitalization of Indigenous and Endangered Languages

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LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Attitudes towards sardinian and italian finally compared via the matched-guise technique

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This paper cites The corpus of Sardinian emigrants:a tool for a quantitative approach to contact phenomena.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models The corpus of Sardinian emigrants:a tool for a quantitative approach to contact phenomena

Reference 39

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LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models The valorisation of african languages and policies in the african education systems: A case of uganda

Reference 40

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LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Revitalizing endangered languages: A practical guide

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This paper cites Building pre-train LLM Dataset for the INDIC Languages: a case study on Hindi.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Building pre-train LLM Dataset for the INDIC Languages: a case study on Hindi

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LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models An empirical analysis of word error rate and keyword error rate

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This paper cites Towards a More Inclusive AI: Progress and Perspectives in Large Language Model Training for the S\'ami Language.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Towards a More Inclusive AI: Progress and Perspectives in Large Language Model Training for the S\'ami Language

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LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Carta dei dialetti d’italia

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LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Appunti per un corpus di sardo multimediale

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This paper cites Applying monte carlo techniques to language identification.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Applying monte carlo techniques to language identification

Reference 47

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This paper cites Robust Speech Recognition via Large-Scale Weak Supervision.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Robust Speech Recognition via Large-Scale Weak Supervision

Reference 48

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This paper cites Sardinian on facebook: Analysing diatopic varieties through translated lexical lists.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Sardinian on facebook: Analysing diatopic varieties through translated lexical lists

Reference 49

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This paper cites Analyzing asr pretraining for low-resource speech-to- text translation.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Analyzing asr pretraining for low-resource speech-to- text translation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:27:35.200966Z

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This paper cites Low-resource taxonomy enrichment with pretrained language models.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Low-resource taxonomy enrichment with pretrained language models

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:27:35.190017Z

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Observation b165687c-bc65-44a2-b1ed-56a33863f69a · outbound

This paper cites End-to-end Text-to-speech for Low-resource Languages by Cross-Lingual Transfer Learning.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models End-to-end Text-to-speech for Low-resource Languages by Cross-Lingual Transfer Learning

Reference 52

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Observation b0939d9e-6307-49a7-8c5e-dc894650fafa · outbound

This paper cites Rule-based machine translation for the italian-sardinian language pair.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Rule-based machine translation for the italian-sardinian language pair

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:27:35.178531Z

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Observation 7bb76d59-520a-457d-a8ca-55500f9d0b80 · outbound

This paper cites Capitolo secondo: Valori, opinioni e atteggiamenti verso le lingue locali.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Capitolo secondo: Valori, opinioni e atteggiamenti verso le lingue locali

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:27:35.167016Z

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Observation b5e8c331-8561-4d1b-b5c1-9038957dd6ef · outbound

This paper cites La lingua e la cultura della Sardegna.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models La lingua e la cultura della Sardegna

Reference 55

Resolution: verified exact; raw_fallback, observed 2026-08-12T16:27:34.733272Z

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Observation f778d27b-2f1e-44e8-8c59-ca03776ff558 · outbound

This paper cites Measuring speech quality for text-to-speech systems: development and assessment of a modified mean opinion score (mos) scale.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Measuring speech quality for text-to-speech systems: development and assessment of a modified mean opinion score (mos) scale

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:27:35.154150Z

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Observation dc183ade-47d8-4f75-96de-18e0cdb7d7a2 · outbound

This paper cites Wagner and Rudolf Virchowstiftung.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Wagner and Rudolf Virchowstiftung

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:27:35.141607Z

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Observation 8bc23a06-23c0-4d71-adca-5205407e58db · outbound

This paper cites Data selection for language models via importance resampling.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Data selection for language models via importance resampling

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:27:35.128450Z

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Observation c70b1709-d956-4a50-9ae3-29974828fd57 · outbound

This paper cites Inducing multilingual text analysis tools via robust projection across aligned corpora.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Inducing multilingual text analysis tools via robust projection across aligned corpora

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:27:35.115092Z

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Observation a646c50a-c68c-4cfd-b7e5-b7a9b5ebb30a · outbound

This paper cites Data collection design for dialogue systems for low-resource languages.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Data collection design for dialogue systems for low-resource languages

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:27:35.101417Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7caba8c4-488c-473f-aa3f-01e37d52c09d · outbound

This paper cites Machine translation with large language models: Prompting, few-shot learning, and fine-tuning with qlora.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Machine translation with large language models: Prompting, few-shot learning, and fine-tuning with qlora

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:27:35.086935Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 06d306f8-66d3-473b-8938-90a8af50e61b · outbound

This paper cites Fine-tuning Large Language Models for Domain-specific Machine Translation.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Fine-tuning Large Language Models for Domain-specific Machine Translation

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-12T16:27:34.553245Z

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Observation 2317cd0c-d392-45cd-bc47-4e02d968148f · outbound

This paper cites Transfer Learning for Low-Resource Neural Machine Translation.

LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models Transfer Learning for Low-Resource Neural Machine Translation

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-12T16:27:34.558239Z

Source-reported events for the cited work

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