Papers by Weiyi Yang
Prototype-Guided Pseudo Labeling for Semi-Supervised Text Classification (2023.acl-long)
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| Challenge: | Existing semi-supervised text classification methods suffer from categorical boundary issues . existing methods suffer by ambiguous categoric boundaries, making it difficult to generate reliable pseudo-labels for each category. |
| Approach: | They propose a semi-supervised framework that assigns pseudo-labels to unlabeled data . they exploit categorical prototypes to assimilate instance representations within the same category . |
| Outcome: | Empirical studies show that the proposed framework is effective . it uses prototypical cluster separation and prototypical-center data selection . |
Learning Sparsity for Effective and Efficient Music Performance Question Answering (2025.acl-short)
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| Challenge: | Existing Music AVQA methods rely on dense and unoptimized representations, leading to inefficiencies in the isolation of key information, reduction of redundancy, and prioritization of critical samples. |
| Approach: | They propose a sparse learning framework specifically designed for Music AVQA to address these challenges. |
| Outcome: | The proposed framework reduces training time by 28.32% while maintaining accuracy while maintaining state-of-the-art performance on the Music AVQA datasets. |
Calibrating Pseudo-Labeling with Class Distribution for Semi-supervised Text Classification (2025.emnlp-main)
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| Challenge: | Existing studies develop effective pseudo-labeling methods, but they struggle with unlabeled data that have imbalanced classes mismatched with the labeled data. |
| Approach: | They propose to use pseudo-labeling to train text classification models with few labeled data and massive unlabeled data. |
| Outcome: | Empirical results show that the proposed model outperforms state-of-the-art methods on 3 common benchmarks. |
DisCo: Distilled Student Models Co-training for Semi-supervised Text Mining (2023.emnlp-main)
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| Challenge: | Existing text mining models are fine-tuned by fine-timing a large pre-trained language model (PLM) in downstream tasks. |
| Approach: | They propose a semi-supervised learning framework for fine-tuning a cohort of small student models generated from a large pre-trained language model using knowledge distillation. |
| Outcome: | The proposed framework outperforms baseline models on semi-supervised text classification and extractive summarization tasks while maintaining comparable performance. |