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Found 11 papers, 4 papers with code
Language Models for Code-switch Detection of te reo Māori and English in a Low-resource Setting
We also show that BiLSTM with pre-trained Māori-English sub-word embeddings outperforms large-scale contextual language models such as BERT on down streaming tasks of detecting Māori language.
Tackling Bias in Pre-trained Language Models: Current Trends and Under-represented Societies
The importance and novelty of this survey are that it explores the perspective of under-represented societies.
Challenges in Annotating Datasets to Quantify Bias in Under-represented Society
Recent advances in artificial intelligence, including the development of highly sophisticated large language models (LLM), have proven beneficial in many real-world applications.
Neuromodulation Gated Transformer
We introduce a novel architecture, the Neuromodulation Gated Transformer (NGT), which is a simple implementation of neuromodulation in transformers via a multiplicative effect.
Effectiveness of Debiasing Techniques: An Indigenous Qualitative Analysis
An indigenous perspective on the effectiveness of debiasing techniques for pre-trained language models (PLMs) is presented in this paper.
The Development of a Labelled te reo Māori-English Bilingual Database for Language Technology
The New Zealand Parliament Hansard debates reports were used to build the database.
Improving Predictions of Tail-end Labels using Concatenated BioMed-Transformers for Long Medical Documents
Improving the tail-end label predictions in multi-label classifications of medical text enables the potential to understand patients better and improve care.
Predicting COVID-19 Patient Shielding: A Comprehensive Study
This study focuses on techniques used for the multi-label classification of medical text.
Seeing The Whole Patient: Using Multi-Label Medical Text Classification Techniques to Enhance Predictions of Medical Codes
We also show that high dimensional embeddings pre-trained using health-related data present a significant improvement in a multi-label setting, similarly to the way they improve performance for binary classification.
Automatic end-to-end De-identification: Is high accuracy the only metric?
De-identification of electronic health records (EHR) is a vital step towards advancing health informatics research and maximising the use of available data.
A survey of automatic de-identification of longitudinal clinical narratives
Use of medical data, also known as electronic health records, in research helps develop and advance medical science.
