12 citations · 40 across the 6 of their papers we have counts for
6 papers
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation
Jixuan Wang, Kuan-Chieh Wang, Frank Rudzicz +1
Large pretrained language models (LMs) like BERT have improved performance in many disparate natural language processing (NLP) tasks. However, fine tuning such models requires a la…
Speaker attribution with voice profiles by graph-based semi-supervised learning
Jixuan Wang, Xiong Xiao, Jian Wu +3
Speaker attribution is required in many real-world applications, such as meeting transcription, where speaker identity is assigned to each utterance according to speaker voice prof…
Speaker diarization with session-level speaker embedding refinement using graph neural networks
Jixuan Wang, Xiong Xiao, Jian Wu +3
Deep speaker embedding models have been commonly used as a building block for speaker diarization systems; however, the speaker embedding model is usually trained according to a gl…
Essential requirements for establishing and operating data trusts: practical guidance based on a working meeting of fifteen Canadian organizations and initiatives
P. Alison Paprica, Eric Sutherland, Andrea Smith +11
Introduction: Increasingly, the label data trust is being applied to repeatable mechanisms or approaches to sharing data in a timely, fair, safe and equitable way. However, there i…
Training without training data: Improving the generalizability of automated medical abbreviation disambiguation
Marta Skreta, Aryan Arbabi, Jixuan Wang +1
Abbreviation disambiguation is important for automated clinical note processing due to the frequent use of abbreviations in clinical settings. Current models for automated abbrevia…
Centroid-based deep metric learning for speaker recognition
Jixuan Wang, Kuan-Chieh Wang, Marc Law +2
Speaker embedding models that utilize neural networks to map utterances to a space where distances reflect similarity between speakers have driven recent progress in the speaker re…