Papers by Evgeny Lagutin

1 papers
Implicit Unlikelihood Training: Improving Neural Text Generation with Reinforcement Learning (2021.eacl-main)

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Challenge: Existing approaches to language modeling use autoregressive methods, but they can produce repetitive results.
Approach: They propose to add a loss function for regularization to avoid unwanted properties, such as contradiction or repetition, to a language model by using policy gradient reinforcement learning.
Outcome: The proposed method reduces repetition without impacting the language model quality.

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