71 citations · 125 across the 13 of their papers we have counts for
13 papers
Rethinking Label Consistency of In-Context Learning: An Implicit Transductive Label Propagation Perspective
Haoyang Chen, Richong Zhang, Junfan Chen
Large language models (LLMs) perform in-context learning (ICL) with minimal supervised examples, which benefits various natural language processing (NLP) tasks. One of the critical…
Improving Subgraph Matching by Combining Algorithms and Graph Neural Networks
Shuyang Guo, Wenjin Xie, Ping Lu +5
Homomorphism is a key mapping technique between graphs that preserves their structure. Given a graph and a pattern, the subgraph homomorphism problem involves finding a mapping fro…
Implicit Word Reordering with Knowledge Distillation for Cross-Lingual Dependency Parsing
Zhuoran Li, Chunming Hu, Junfan Chen +2
Word order difference between source and target languages is a major obstacle to cross-lingual transfer, especially in the dependency parsing task. Current works are mostly based o…
Progressively Modality Freezing for Multi-Modal Entity Alignment
Yani Huang, Xuefeng Zhang, Richong Zhang +2
Multi-Modal Entity Alignment aims to discover identical entities across heterogeneous knowledge graphs. While recent studies have delved into fusion paradigms to represent entities…
Improving Zero-Shot Cross-Lingual Transfer via Progressive Code-Switching
Zhuoran Li, Chunming Hu, Junfan Chen +3
Code-switching is a data augmentation scheme mixing words from multiple languages into source lingual text. It has achieved considerable generalization performance of cross-lingual…
Anaphor Assisted Document-Level Relation Extraction
Chonggang Lu, Richong Zhang, Kai Sun +3
Document-level relation extraction (DocRE) involves identifying relations between entities distributed in multiple sentences within a document. Existing methods focus on building a…
Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic
Keqin Chen, Zhao Zhang, Weili Zeng +3
In human conversations, individuals can indicate relevant regions within a scene while addressing others. In turn, the other person can then respond by referring to specific region…
ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification
Junfan Chen, Richong Zhang, Yongyi Mao +1
Few-shot text classification has recently been promoted by the meta-learning paradigm which aims to identify target classes with knowledge transferred from source classes with sets…
Adversarial Word Dilution as Text Data Augmentation in Low-Resource Regime
Junfan Chen, Richong Zhang, Zheyan Luo +2
Data augmentation is widely used in text classification, especially in the low-resource regime where a few examples for each class are available during training. Despite the succes…
Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction
Kai Sun, Richong Zhang, Samuel Mensah +3
Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental task in opinion mining that aims to extract the targets (or aspects) on which opinions have been expresse…
Cross Domain Few-Shot Learning via Meta Adversarial Training
Jirui Qi, Richong Zhang, Chune Li +1
Few-shot relation classification (RC) is one of the critical problems in machine learning. Current research merely focuses on the set-ups that both training and testing are from th…
Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation
Qianben Chen, Richong Zhang, Yaowei Zheng +1
Contrastive learning has achieved remarkable success in representation learning via self-supervision in unsupervised settings. However, effectively adapting contrastive learning to…
Robust Regularization with Adversarial Labelling of Perturbed Samples
Xiaohui Guo, Richong Zhang, Yaowei Zheng +1
Recent researches have suggested that the predictive accuracy of neural network may contend with its adversarial robustness. This presents challenges in designing effective regular…