Papers by Dongwon Ryu

2 papers
Multilingual Simultaneous Neural Machine Translation (2021.findings-acl)

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Challenge: Simultaneous machine translation (SIMT) involves translating source utterances to the target language in real-time before the speaker utterrance completes.
Approach: They propose a multilingual approach to simultaneous machine translation where a single model simultaneously translates between multiple languages.
Outcome: The proposed multilingual approach improves on two Germanic and three Romance languages and is on-par or better than the universal model trained for all languages.
Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based Games (2022.acl-short)

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Challenge: Existing RL agents are far away from solving text-based games due to their combinatorially large action spaces that hinders efficient exploration.
Approach: They propose an exploration technique that injects external commonsense knowledge, via a pretrained language model, into the agent during training when the agent is the most uncertain about its next action.
Outcome: The proposed method exhibits improvement on the collected game scores during the training in four out of nine games from Jericho.

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