Teacher Forcing Recovers Reward Functions for Text Generation

• URL: http://arxiv.org/abs/2210.08708v1
• Date: Mon, 17 Oct 2022 02:48:58 GMT
• Title: Teacher Forcing Recovers Reward Functions for Text Generation
• Authors: Yongchang Hao, Yuxin Liu, Lili Mou
• Abstract summary: We propose a task-agnostic approach that derives a step-wise reward function directly from a model trained with teacher forcing. We additionally propose a simple modification to stabilize the RL training on non-parallel datasets with our induced reward function.
• Score: 21.186397113834506
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement learning (RL) has been widely used in text generation to alleviate the exposure bias issue or to utilize non-parallel datasets. The reward function plays an important role in making RL training successful. However, previous reward functions are typically task-specific and sparse, restricting the use of RL. In our work, we propose a task-agnostic approach that derives a step-wise reward function directly from a model trained with teacher forcing. We additionally propose a simple modification to stabilize the RL training on non-parallel datasets with our induced reward function. Empirical results show that our method outperforms self-training and reward regression methods on several text generation tasks, confirming the effectiveness of our reward function.

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