Papers by Juan Zha
Disentangling Task Relations for Few-shot Text Classification via Self-Supervised Hierarchical Task Clustering (2022.findings-emnlp)
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| Challenge: | Existing methods for Few-Shot Text Classification are limited by their global knowledge-shared mechanisms. |
| Approach: | They propose a self-supervised hierarchical task clustering method to address task heterogeneity . they use prior knowledge from historical tasks to leverage prior knowledge . |
| Outcome: | The proposed method can learn a classifier efficiently with few examples . it disentangles the underlying relations between tasks to improve interpretability . |
Eliciting and Understanding Cross-task Skills with Task-level Mixture-of-Experts (2022.findings-emnlp)
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| Challenge: | Pre-trained transformer models are capable of multitasking on diverse NLP tasks, but little is known about how multitaskability and cross-task generalization is achieved. |
| Approach: | They propose to use a transformer-based mixture-of-expert model with a router component to choose among experts dynamically and flexibly. |
| Outcome: | The proposed models improve the average performance gain (ARG) metric by 2.6% when adapting to unseen tasks, and by 5.6% in zero-shot generalization settings. |