activity
20202024
most citedKG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques

5 citations · 15 across the 10 of their papers we have counts for

collaborators
Showing 2022Show all

6 papers · 1 filter

cs.CL2022★ 1 cited

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…

cs.SD2022

Filter and evolve: progressive pseudo label refining for semi-supervised automatic speech recognition

Zezhong Jin, Dading Zhong, Xiao Song +3

Fine tuning self supervised pretrained models using pseudo labels can effectively improve speech recognition performance. But, low quality pseudo labels can misguide decision bound…

cs.CL2022★ 4 cited

A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectives

Qingcheng Zeng, An-Ran Li

Irony is a ubiquitous figurative language in daily communication. Previously, many researchers have approached irony from linguistic, cognitive science, and computational aspects.…

cs.CL2022

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…

cs.CL2022

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…

cs.SD2022★ 1 cited

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…