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When communicating, people behave consistently across conversational roles:People understand the words they say and are able to produce the words theyhear. To date, artificial agents developed for language tasks have lacked suchsymmetry, meaning agents trained to produce language are unable to understandit and vice-versa. In this work, we exploit the symmetric nature ofcommunication in order to improve both the efficiency and quality of languageacquisition in learning agents. Specifically, we consider the setting in whichan agent must learn to both understand and generate words in an existinglanguage, but with the assumption that access to interaction with "oracle"speakers of the language is very limited. We show that using self-play as asubstitute for direct supervision enables the agent to transfer its knowledgeacross roles (e.g. training as a listener but testing as a speaker) and makebetter inferences about the ground truth lexicon using only a handful ofinteractions with the oracle.

Self-play for Data Efficient Language Acquisition