Papers by Junqiu Wei
Hypoformer: Hybrid Decomposition Transformer for Edge-friendly Neural Machine Translation (2022.emnlp-main)
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| Challenge: | Existing methods to compress Transformer are limited to sub-components, e.g., selfattention networks or embedding layer. |
| Approach: | They propose a Hybrid Tensor-Train decomposition which retains full rank and meanwhile reduces operations and parameters. |
| Outcome: | The proposed model outperforms light-weight SOTA methods on three translation tasks and achieves 7.1 points absolute improvement in BLEU and 1.27 X speedup on IWSLT’14 De-En task. |
ClusterFormer: Neural Clustering Attention for Efficient and Effective Transformer (2022.acl-long)
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| Challenge: | Existing sparse attention methods use fixed patterns to select words without considering similarities between words. |
| Approach: | They propose a neural clustering method which integrates into the Self-Attention Mechanism in Transformer and integrates it into the target task. |
| Outcome: | The proposed method outperforms two typical sparse attention methods on translation, text classification, and text matching tasks while having a comparable or even better time and memory efficiency. |