41 citations · 187 across the 25 of their papers we have counts for
7 papers · 1 filter
GreenPLM: Cross-Lingual Transfer of Monolingual Pre-Trained Language Models at Almost No Cost
Qingcheng Zeng, Lucas Garay, Peilin Zhou +7
Large pre-trained models have revolutionized natural language processing (NLP) research and applications, but high training costs and limited data resources have prevented their be…
Equivariant Contrastive Learning for Sequential Recommendation
Peilin Zhou, Jingqi Gao, Yueqi Xie +5
Contrastive learning (CL) benefits the training of sequential recommendation models with informative self-supervision signals. Existing solutions apply general sequential data augm…
METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets
Peilin Zhou, Zeqiang Wang, Dading Chong +6
The COVID-19 pandemic continues to bring up various topics discussed or debated on social media. In order to explore the impact of pandemics on people's lives, it is crucial to und…
Low-resource Accent Classification in Geographically-proximate Settings: A Forensic and Sociophonetics Perspective
Qingcheng Zeng, Dading Chong, Peilin Zhou +1
Accented speech recognition and accent classification are relatively under-explored research areas in speech technology. Recently, deep learning-based methods and Transformer-based…
Calibrate and Refine! A Novel and Agile Framework for ASR-error Robust Intent Detection
Peilin Zhou, Dading Chong, Helin Wang +1
The past ten years have witnessed the rapid development of text-based intent detection, whose benchmark performances have already been taken to a remarkable level by deep learning…
Masked Spectrogram Prediction For Self-Supervised Audio Pre-Training
Dading Chong, Helin Wang, Peilin Zhou +1
Transformer-based models attain excellent results and generalize well when trained on sufficient amounts of data. However, constrained by the limited data available in the audio do…
Decoupled Side Information Fusion for Sequential Recommendation
Yueqi Xie, Peilin Zhou, Sunghun Kim
Side information fusion for sequential recommendation (SR) aims to effectively leverage various side information to enhance the performance of next-item prediction. Most state-of-t…