activity
20122023
most citedAugmenting Data with Mixup for Sentence Classification: An Empirical Study

145 citations · 235 across the 22 of their papers we have counts for

collaborators
Showing cs.CLShow all

10 papers · 1 filter

cs.CL2023

Narrowing the Gap between Supervised and Unsupervised Sentence Representation Learning with Large Language Model

Mingxin Li, Richong Zhang, Zhijie Nie +1

Sentence Representation Learning (SRL) is a fundamental task in Natural Language Processing (NLP), with the Contrastive Learning of Sentence Embeddings (CSE) being the mainstream t…

cs.CL2023★ 1 cited

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…

cs.CL2023★ 2 cited

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…

cs.CL2023★ 2 cited

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…

cs.CL2022★ 47 cited

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…

cs.CL2020

Parallel Interactive Networks for Multi-Domain Dialogue State Generation

Junfan Chen, Richong Zhang, Yongyi Mao +1

The dependencies between system and user utterances in the same turn and across different turns are not fully considered in existing multidomain dialogue state tracking (MDST) mode…