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Found 8 papers, 3 papers with code
Evaluating Word Embeddings in Extremely Under-Resourced Languages: A Case Study in Bribri
Future research needs to further explore the patterns of morphological/structural learning, to examine the behavior of deep learning embeddings, and to establish a human baseline.
Explicit Tone Transcription Improves ASR Performance in Extremely Low-Resource Languages: A Case Study in Bribri
Linguistic tone is transcribed for input into ASR systems in numerous ways.
Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas
This paper presents the results of the 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas.
Development of Automatic Speech Recognition for the Documentation of Cook Islands Māori
This paper describes the process of data processing and training of an automatic speech recognition (ASR) system for Cook Islands Māori (CIM), an Indigenous language spoken by approximately 22, 000 people in the South Pacific.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Meeting the Needs of Low-Resource Languages: The Value of Automatic Alignments via Pretrained Models
However, the languages most in need of automatic alignment are low-resource and, thus, not typically included in the pretraining data.
AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages
Continued pretraining offers improvements, with an average accuracy of 44. 05%.
Neural Machine Translation Models with Back-Translation for the Extremely Low-Resource Indigenous Language Bribri
This paper presents a neural machine translation model and dataset for the Chibchan language Bribri, with an average performance of BLEU 16. 9{\mbox{$\pm$}}1. 7.
