13 citations · 24 across the 4 of their papers we have counts for
4 papers
A Rationale-Centric Framework for Human-in-the-loop Machine Learning
Jinghui Lu, Linyi Yang, Brian Mac Namee +1
We present a novel rationale-centric framework with human-in-the-loop -- Rationales-centric Double-robustness Learning (RDL) -- to boost model out-of-distribution performance in fe…
A Sentence-level Hierarchical BERT Model for Document Classification with Limited Labelled Data
Jinghui Lu, Maeve Henchion, Ivan Bacher +1
Training deep learning models with limited labelled data is an attractive scenario for many NLP tasks, including document classification. While with the recent emergence of BERT, d…
Investigating the Effectiveness of Representations Based on Pretrained Transformer-based Language Models in Active Learning for Labelling Text Datasets
Jinghui Lu, Brian MacNamee
Active learning has been shown to be an effective way to alleviate some of the effort required in utilising large collections of unlabelled data for machine learning tasks without…
Investigating the Effectiveness of Representations Based on Word-Embeddings in Active Learning for Labelling Text Datasets
Jinghui Lu, Maeve Henchion, Brian Mac Namee
Manually labelling large collections of text data is a time-consuming, expensive, and laborious task, but one that is necessary to support machine learning based on text datasets.…