Papers by Jianhai Zhang
MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification (2022.naacl-main)
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| Challenge: | Existing methods for text classification fail to generalize to unseen classes with very few labeled text instances per class. |
| Approach: | They propose a meta-learning method which performs instance-wise comparison followed by aggregation to generate class-wise matching vectors instead of prototype learning. |
| Outcome: | Experiments show that the proposed method outperforms existing methods under both the standard and generalized FSL settings. |
Simple and Effective Text Matching with Richer Alignment Features (P19-1)
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| Challenge: | Existing models only use a single inter-sequence alignment layer to make full use of this process. |
| Approach: | They propose to keep three key features available for inter-sequence alignment . they conduct experiments on four well-studied benchmark datasets . |
| Outcome: | The proposed model is able to perform on four well-studied datasets with fewer parameters and the inference speed is at least 6 times faster than similar models. |