113 citations · 240 across the 9 of their papers we have counts for
10 papers
Open-set Recognition via Augmentation-based Similarity Learning
Sepideh Esmaeilpour, Lei Shu, Bing Liu
The primary assumption of conventional supervised learning or classification is that the test samples are drawn from the same distribution as the training samples, which is called…
Continual Learning with Knowledge Transfer for Sentiment Classification
Zixuan Ke, Bing Liu, Hao Wang +1
This paper studies continual learning (CL) for sentiment classification (SC). In this setting, the CL system learns a sequence of SC tasks incrementally in a neural network, where…
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks
Zixuan Ke, Bing Liu, Hu Xu +1
This paper studies continual learning (CL) of a sequence of aspect sentiment classification(ASC) tasks in a particular CL setting called domain incremental learning (DIL). Each tas…
Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning
Zixuan Ke, Bing Liu, Nianzu Ma +2
Continual learning (CL) learns a sequence of tasks incrementally with the goal of achieving two main objectives: overcoming catastrophic forgetting (CF) and encouraging knowledge t…
Tea Chrysanthemum Detection under Unstructured Environments Using the TC-YOLO Model
Chao Qi, Junfeng Gao, Simon Pearson +3
Tea chrysanthemum detection at its flowering stage is one of the key components for selective chrysanthemum harvesting robot development. However, it is a challenge to detect flowe…
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…
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System
Yixuan Su, Lei Shu, Elman Mansimov +4
Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems. Despite their success, existing methods often formulate this task as a cascade…
Zero-Shot Out-of-Distribution Detection Based on the Pre-trained Model CLIP
Sepideh Esmaeilpour, Bing Liu, Eric Robertson +1
In an out-of-distribution (OOD) detection problem, samples of known classes(also called in-distribution classes) are used to train a special classifier. In testing, the classifier…
Controllable Text Generation with Focused Variation
Lei Shu, Alexandros Papangelis, Yi-Chia Wang +5
This work introduces Focused-Variation Network (FVN), a novel model to control language generation. The main problems in previous controlled language generation models range from t…
DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis
Hu Xu, Bing Liu, Lei Shu +1
This paper focuses on learning domain-oriented language models driven by end tasks, which aims to combine the worlds of both general-purpose language models (such as ELMo and BERT)…