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Found 7 papers, 1 papers with code
The eBible Corpus: Data and Model Benchmarks for Bible Translation for Low-Resource Languages
Efficiently and accurately translating a corpus into a low-resource language remains a challenge, regardless of the strategies employed, whether manual, automated, or a combination of the two.
Adapting to the Low-Resource Double-Bind: Investigating Low-Compute Methods on Low-Resource African Languages
In this work, we explore the applicability of low-compute approaches such as language adapters in the context of this low-resource double-bind.
User Study for Improving Tools for Bible Translation
Technology has increasingly become an integral part of the Bible translation process.
Snow Mountain: Dataset of Audio Recordings of The Bible in Low Resource Languages
Automatic Speech Recognition (ASR) has increasing utility in the modern world.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Federated Named Entity Recognition
We present an analysis of the performance of Federated Learning in a paradigmatic natural-language processing task: Named-Entity Recognition (NER).
Biomedical Named Entity Recognition via Reference-Set Augmented Bootstrapping
Second, we use a reference set of entity names (e. g., proteins in UniProt) to identify entity mentions with high precision, but low recall, on an unlabeled corpus.
NSEEN: Neural Semantic Embedding for Entity Normalization
An important task in this process is entity normalization, which consists of mapping noisy entity mentions in text to canonical entities in well-known reference sets.
