Papers by Atijit Anuchitanukul

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Challenge:	Reinforcement Learning (RL) is dependent on the reward formulation due to the intrinsic difficulty of the task in the high-dimensional discrete action space and the sparseness of the standard reward functions.
Approach:	They propose a maximally dense semantic-level unsupervised reward function which mimics human evaluation by considering both sentence fluency and semantic similarity.
Outcome:	The proposed reward outperforms the standard sparse reward by 2% on average for in- and out-of-domain settings.