13 citations · 39 across the 9 of their papers we have counts for
9 papers
UniDoc: A Universal Large Multimodal Model for Simultaneous Text Detection, Recognition, Spotting and Understanding
Hao Feng, Zijian Wang, Jingqun Tang +4
In the era of Large Language Models (LLMs), tremendous strides have been made in the field of multimodal understanding. However, existing advanced algorithms are limited to effecti…
Deeply Coupled Cross-Modal Prompt Learning
Xuejing Liu, Wei Tang, Jinghui Lu +3
Recent advancements in multimodal foundation models (e.g., CLIP) have excelled in zero-shot generalization. Prompt tuning involved in the knowledge transfer from foundation models…
PUnifiedNER: A Prompting-based Unified NER System for Diverse Datasets
Jinghui Lu, Rui Zhao, Brian Mac Namee +1
Much of named entity recognition (NER) research focuses on developing dataset-specific models based on data from the domain of interest, and a limited set of related entity types.…
SDA: Simple Discrete Augmentation for Contrastive Sentence Representation Learning
Dongsheng Zhu, Zhenyu Mao, Jinghui Lu +2
Contrastive learning has recently achieved compelling performance in unsupervised sentence representation. As an essential element, data augmentation protocols, however, have not b…
What Makes Pre-trained Language Models Better Zero-shot Learners?
Jinghui Lu, Dongsheng Zhu, Weidong Han +3
Current methods for prompt learning in zeroshot scenarios widely rely on a development set with sufficient human-annotated data to select the best-performing prompt template a post…
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.…