GIRL: Reward Function Learning Framework Independent of Text Generator Samples for Reinforcement Learning in Text Generation Tasks

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  • 文書生成タスクに対する強化学習応用における文書生成器のサンプルに非依存な報酬関数学習フレームワークの提案

Abstract

In text generation tasks, reinforcement learning is known to be an effective method. Previous research have attempted to learn from data using samples from the text generator. This paper addresses the problem of not being able to quantitatively visualize the progress of the generator's training, caused by the dependency of generator's samples. We propose a framework called Generator-independent Reward Learning, which does not use any samples while learning reward function. We confirmed that our method based on the framework can quantitatively visualize the learning of the text generator and surpass the performance of baseline methods.

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