63 citations · 147 across the 24 of their papers we have counts for
27 papers
T-Retrievability: A Topic-Focused Approach to Measure Fair Document Exposure in Information Retrieval
Xuejun Chang, Zaiqiao Meng, Debasis Ganguly
Retrievability of a document is a collection-based statistic that measures its expected (reciprocal) rank of being retrieved within a specific rank cut-off. A collection with unifo…
TRACE the Evidence: Constructing Knowledge-Grounded Reasoning Chains for Retrieval-Augmented Generation
Jinyuan Fang, Zaiqiao Meng, Craig Macdonald
Retrieval-augmented generation (RAG) offers an effective approach for addressing question answering (QA) tasks. However, the imperfections of the retrievers in RAG models often res…
Weakly Supervised Deep Hyperspherical Quantization for Image Retrieval
Jinpeng Wang, Bin Chen, Qiang Zhang +3
Deep quantization methods have shown high efficiency on large-scale image retrieval. However, current models heavily rely on ground-truth information, hindering the application of…
CLEX: Continuous Length Extrapolation for Large Language Models
Guanzheng Chen, Xin Li, Zaiqiao Meng +2
Transformer-based Large Language Models (LLMs) are pioneering advances in many natural language processing tasks, however, their exceptional capabilities are restricted within the…
When hard negative sampling meets supervised contrastive learning
Zijun Long, George Killick, Richard McCreadie +2
State-of-the-art image models predominantly follow a two-stage strategy: pre-training on large datasets and fine-tuning with cross-entropy loss. Many studies have shown that using…
Sparkles: Unlocking Chats Across Multiple Images for Multimodal Instruction-Following Models
Yupan Huang, Zaiqiao Meng, Fangyu Liu +3
Large language models exhibit enhanced zero-shot performance on various tasks when fine-tuned with instruction-following data. Multimodal instruction-following models extend these…
Label Denoising through Cross-Model Agreement
Yu Wang, Xin Xin, Zaiqiao Meng +2
Learning from corrupted labels is very common in real-world machine-learning applications. Memorizing such noisy labels could affect the learning of the model, leading to sub-optim…
BAND: Biomedical Alert News Dataset
Zihao Fu, Meiru Zhang, Zaiqiao Meng +3
Infectious disease outbreaks continue to pose a significant threat to human health and well-being. To improve disease surveillance and understanding of disease spread, several surv…
Biomedical Named Entity Recognition via Dictionary-based Synonym Generalization
Zihao Fu, Yixuan Su, Zaiqiao Meng +1
Biomedical named entity recognition is one of the core tasks in biomedical natural language processing (BioNLP). To tackle this task, numerous supervised/distantly supervised appro…
COFFEE: A Contrastive Oracle-Free Framework for Event Extraction
Meiru Zhang, Yixuan Su, Zaiqiao Meng +2
Event extraction is a complex information extraction task that involves extracting events from unstructured text. Prior classification-based methods require comprehensive entity an…
Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applications
Muhammad Arslan Manzoor, Sarah Albarri, Ziting Xian +3
Multimodality Representation Learning, as a technique of learning to embed information from different modalities and their correlations, has achieved remarkable success on a variet…
Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces
Jiahang Cao, Jinyuan Fang, Zaiqiao Meng +1
Knowledge graph embedding (KGE) is an increasingly popular technique that aims to represent entities and relations of knowledge graphs into low-dimensional semantic spaces for a wi…
Can Pretrained Language Models (Yet) Reason Deductively?
Zhangdie Yuan, Songbo Hu, Ivan Vulić +2
Acquiring factual knowledge with Pretrained Language Models (PLMs) has attracted increasing attention, showing promising performance in many knowledge-intensive tasks. Their good p…
Revisiting Parameter-Efficient Tuning: Are We Really There Yet?
Guanzheng Chen, Fangyu Liu, Zaiqiao Meng +1
Parameter-Efficient Tuning (PETuning) methods have been deemed by many as the new paradigm for using pretrained language models (PLMs). By tuning just a fraction amount of paramete…
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning
Yixuan Su, Fangyu Liu, Zaiqiao Meng +4
Masked language models (MLMs) such as BERT and RoBERTa have revolutionized the field of Natural Language Understanding in the past few years. However, existing pre-trained MLMs oft…
Rewire-then-Probe: A Contrastive Recipe for Probing Biomedical Knowledge of Pre-trained Language Models
Zaiqiao Meng, Fangyu Liu, Ehsan Shareghi +3
Knowledge probing is crucial for understanding the knowledge transfer mechanism behind the pre-trained language models (PLMs). Despite the growing progress of probing knowledge for…
Mixture-of-Partitions: Infusing Large Biomedical Knowledge Graphs into BERT
Zaiqiao Meng, Fangyu Liu, Thomas Hikaru Clark +2
Infusing factual knowledge into pre-trained models is fundamental for many knowledge-intensive tasks. In this paper, we proposed Mixture-of-Partitions (MoP), an infusion approach t…
Few-Shot Table-to-Text Generation with Prototype Memory
Yixuan Su, Zaiqiao Meng, Simon Baker +1
Neural table-to-text generation models have achieved remarkable progress on an array of tasks. However, due to the data-hungry nature of neural models, their performances strongly…
Learning to Detect Few-Shot-Few-Clue Misinformation
Qiang Zhang, Hongbin Huang, Shangsong Liang +2
The quality of digital information on the web has been disquieting due to the lack of careful manual review. Consequently, a large volume of false textual information has been diss…
Graph Neural Pre-training for Enhancing Recommendations using Side Information
Zaiqiao Meng, Siwei Liu, Craig Macdonald +1
Leveraging the side information associated with entities (i.e. users and items) to enhance the performance of recommendation systems has been widely recognized as an important mode…
Learning Robust Recommenders through Cross-Model Agreement
Yu Wang, Xin Xin, Zaiqiao Meng +3
Learning from implicit feedback is one of the most common cases in the application of recommender systems. Generally speaking, interacted examples are considered as positive while…
Self-Alignment Pretraining for Biomedical Entity Representations
Fangyu Liu, Ehsan Shareghi, Zaiqiao Meng +2
Despite the widespread success of self-supervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remai…
Exploring Data Splitting Strategies for the Evaluation of Recommendation Models
Zaiqiao Meng, Richard McCreadie, Craig Macdonald +1
Effective methodologies for evaluating recommender systems are critical, so that such systems can be compared in a sound manner. A commonly overlooked aspect of recommender system…
Semi-supervisedly Co-embedding Attributed Networks
Zaiqiao Meng, Shangsong Liang, Jinyuan Fang +1
Deep generative models (DGMs) have achieved remarkable advances. Semi-supervised variational auto-encoders (SVAE) as a classical DGM offer a principled framework to effectively gen…