Learning to Selectively Learn for Weakly Supervised Paraphrase Generation with Model-based Reinforcement Learning (2022.naacl-main)
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| Challenge: | Paraphrase generation is an important natural language generation task . however, the effectiveness of paraphrase generation can be limited due to the limited data available. |
| Approach: | They propose a weakly supervised approach to paraphrase generation that leverages reinforcement learning for effective model training with data selection. |
| Outcome: | The proposed model improves the state-of-the-art performance on four weakly supervised paraphrase generation tasks. |
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