9 citations · 9 across the 1 of their papers we have counts for
5 papers
An Unsupervised Sentence Embedding Method by Mutual Information Maximization
Yan Zhang, Ruidan He, Zuozhu Liu +2
BERT is inefficient for sentence-pair tasks such as clustering or semantic search as it needs to evaluate combinatorially many sentence pairs which is very time-consuming. Sentence…
Feature Adaptation of Pre-Trained Language Models across Languages and Domains with Robust Self-Training
Hai Ye, Qingyu Tan, Ruidan He +3
Adapting pre-trained language models (PrLMs) (e.g., BERT) to new domains has gained much attention recently. Instead of fine-tuning PrLMs as done in most previous work, we investig…
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis
Ruidan He, Wee Sun Lee, Hwee Tou Ng +1
Aspect-based sentiment analysis produces a list of aspect terms and their corresponding sentiments for a natural language sentence. This task is usually done in a pipeline manner,…
Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification
Ruidan He, Wee Sun Lee, Hwee Tou Ng +1
We consider the cross-domain sentiment classification problem, where a sentiment classifier is to be learned from a source domain and to be generalized to a target domain. Our appr…
Exploiting Document Knowledge for Aspect-level Sentiment Classification
Ruidan He, Wee Sun Lee, Hwee Tou Ng +1
Attention-based long short-term memory (LSTM) networks have proven to be useful in aspect-level sentiment classification. However, due to the difficulties in annotating aspect-leve…