Papers with dynamic unsupervised reward function
Exploring Supervised and Unsupervised Rewards in Machine Translation (2021.eacl-main)
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| Challenge: | Autoregressive sequence-to-sequence (seq2sequ) neural architectures have become the de facto approach in Machine Translation (MT). |
| Approach: | They propose to make models less reliant on cross-entropy loss and evaluation metrics . they propose an entropicity-regularised RL method that explores the action space . |
| Outcome: | The proposed method exploits the action space and unsupervised reward function to balance between exploration and exploitation. |