Papers with token-level loss smoothing
Token-level and sequence-level loss smoothing for RNN language models (P18-1)
Copied to clipboard
| Challenge: | Maximum likelihood estimation treats all sentences that do not match the ground truth as equally poor, ignoring the structure of the output space. |
| Approach: | They propose to extend the reward augmented maximum likelihood approach to token-level loss smoothing by using token-based approaches to improve the model's performance. |
| Outcome: | The proposed model improves on image captioning and machine translation tasks and treats all sentences that do not match the ground truth as poor . |