  The migration of conversational AI agents across different embodiments in
order to maintain the continuity of the task has been recently explored to
further improve user experience. However, these migratable agents lack
contextual understanding of the user information and the migrated device during
the dialog conversations with the user. This opens the question of how an agent
might behave when migrated into an embodiment for contextually predicting the
next utterance. We collected a dataset from the dialog conversations between
crowdsourced workers with the migration context involving personal and
non-personal utterances in different settings (public or private) of embodiment
into which the agent migrated. We trained the generative and information
retrieval models on the dataset using with and without migration context and
report the results of both qualitative metrics and human evaluation. We believe
that the migration dataset would be useful for training future migratable AI
systems.
