Concurrent Acquisition of the Meaning of Sentence-Final Particles and Nouns Through Human-Robot Interaction

Sentence-final particles serve an important role in spoken Japanese, because they express the speaker's mental attitudes toward a proposition and/or an interlocutor. They are acquired at early ages and occur very frequently in everyday conversation. However, there has been little proposal for a computational model of the acquisition of sentence-final particles. In this paper, we report on a study in which a robot learns how to react to utterances that have a sentence-final particle and gives appropriate responses based on rewards given by an interlocutor, and at the same time, learns the meaning of nouns. Preliminary experimental result shows that the robot learns to react correctly in response to yo, which expresses the speaker's intention to communicate new information, and to ne, which denotes the speaker's desire to confirm that some information is shared, and also learns the correct referents of nouns.