Papers by Xi’ao Su
Contrastive Learning-Enhanced Nearest Neighbor Mechanism for Multi-Label Text Classification (2022.acl-short)
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| Challenge: | Existing methods for multi-label text classification neglect the knowledge from the existing similar instances when predicting labels of a specific text. |
| Approach: | They propose a k nearest neighbor mechanism which retrieves several neighbor instances and interpolates the model output with their labels. |
| Outcome: | Extensive experiments show that the proposed method can bring significant performance improvements to multiple MLTC models including state-of-the-art pretrained and non-pretrained ones. |
Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification (2021.emnlp-main)
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| Challenge: | Large-scale multi-label text classification tasks often face long-tailed label distributions, where many labels have few or even no training instances. |
| Approach: | They propose a meta-learning approach that incorporates the objective of adapting to new low-resource tasks into the meta-Learning phase. |
| Outcome: | The proposed approach achieves state-of-the-art against strong baselines and can still enhance powerful BERTlike models. |