Papers by Fanglan Chen
Mitigating Uncertainty in Document Classification (N19-1)
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| Challenge: | Existing models for uncertainty measurement are time-consuming and unable to handle large-scale data sets. |
| Approach: | They propose a new dropout-entropy method for uncertainty measurement and a metric learning method on feature representations to boost the performance of dropout based uncertainty methods. |
| Outcome: | The proposed method improves accuracy from 0.78 to 0.92 when 30% of the most uncertain predictions were handed over to human experts in “20NewsGroup” data. |
Uncertainty-Aware Cross-Lingual Transfer with Pseudo Partial Labels (2022.findings-naacl)
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| Challenge: | Existing methods to train pre-trained language models for zero-shot cross-lingual tasks are noisy and lack confidence. |
| Approach: | They propose an uncertainty-aware cross-lingual transfer framework with pseudo-partial-label to maximize the utilization of unlabeled data by reducing noise. |
| Outcome: | The proposed framework outperforms baselines on named entity recognition and natural language inference tasks on 40 languages. |
TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation (2023.acl-long)
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| Challenge: | Existing methods for fewshot text classification depend on inter-class variance . Existing approaches suffer from MLADA, which performs poorly on tasks with high inter- class variance whereas it fails to distinguish samples from tasks with low inter-group variance. |
| Approach: | They propose a task-adaptive reference transformation network to transform class prototypes to per-class fixed reference points in task-adapted metric spaces. |
| Outcome: | The proposed method surpasses state-of-the-art methods in 1-shot and 5-shot classifications on the 20 Newsgroups dataset. |
Towards More Accurate Uncertainty Estimation In Text Classification (2020.emnlp-main)
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Jianfeng He, Xuchao Zhang, Shuo Lei, Zhiqian Chen, Fanglan Chen, Abdulaziz Alhamadani, Bei Xiao, ChangTien Lu
| Challenge: | Existing models of uncertainty score depend on winning score, which is the maximum probability in a semantic vector. |
| Approach: | They propose to generate accurate uncertainty score by improving the confidence of winning scores by reducing the effect of overconfidence of winning score and considering the impact of different categories simultaneously. |
| Outcome: | The proposed model reduces the effect of overconfidence of winning score and considers impact of different categories of uncertainty simultaneously. |