Revisiting the Weaknesses of Reinforcement Learning for Neural Machine Translation (2021.naacl-main)
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| Challenge: | In neural sequence-to-sequence learning, Reinforcement Learning (RL) has gained popularity due to the suitability of Policy Gradient (PG) methods for the end-to end training paradigm. |
| Approach: | They propose to let the model explore the output space beyond the reference output that is used for standard cross-entropy minimization by reinforcing model outputs according to their quality, effectively increasing the likelihood of higher-quality samples. |
| Outcome: | The proposed model explores the output space beyond the reference output that is used for cross-entropy minimization, increasing the likelihood of higher-quality samples. |
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