Dongwon Ryu


Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based Games
Dongwon Ryu | Ehsan Shareghi | Meng Fang | Yunqiu Xu | Shirui Pan | Reza Haf
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Text-based games (TGs) are exciting testbeds for developing deep reinforcement learning techniques due to their partially observed environments and large action spaces. In these games, the agent learns to explore the environment via natural language interactions with the game simulator. A fundamental challenge in TGs is the efficient exploration of the large action space when the agent has not yet acquired enough knowledge about the environment. We propose CommExpl, an exploration technique that injects external commonsense knowledge, via a pretrained language model (LM), into the agent during training when the agent is the most uncertain about its next action. Our method exhibits improvement on the collected game scores during the training in four out of nine games from Jericho. Additionally, the produced trajectory of actions exhibit lower perplexity, when tested with a pretrained LM, indicating better closeness to human language.


Multilingual Simultaneous Neural Machine Translation
Philip Arthur | Dongwon Ryu | Gholamreza Haffari
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021