Papers with language generation applications
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. |
Societal Biases in Language Generation: Progress and Challenges (2021.acl-long)
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| Challenge: | Language generation techniques can produce undesirable societal biases that can negatively impact marginalized populations. |
| Approach: | They propose to examine how decoding techniques contribute to biases in language generation . they also conduct experiments to quantify the effects of these techniques . |
| Outcome: | The proposed methods can reduce biases and improve user experience, the authors argue . they also show that the proposed techniques can reduce societal biase . |