Papers by Rizhao Fan
Learning to Adapt to Low-Resource Paraphrase Generation (2022.emnlp-main)
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| Challenge: | Conventional approaches to paraphrase generation often rely on a large number of parallel paraphrases, which require a lot of domain knowledge. |
| Approach: | They propose an adapter for paraphrase generation models optimized by meta-learning to overcome domain shifting problem when training on scarce labeled data. |
| Outcome: | The proposed model achieves state-of-the-art on three benchmark datasets. |