14 citations · 23 across the 3 of their papers we have counts for
7 papers · 1 filter
Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation
Qingyu Tan, Ruidan He, Lidong Bing +1
Document-level Relation Extraction (DocRE) is a more challenging task compared to its sentence-level counterpart. It aims to extract relations from multiple sentences at once. In t…
On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation
Ruidan He, Linlin Liu, Hai Ye +6
Adapter-based tuning has recently arisen as an alternative to fine-tuning. It works by adding light-weight adapter modules to a pretrained language model (PrLM) and only updating t…
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…