  There is a growing collection of work analyzing and mitigating societal
biases in language understanding, generation, and retrieval tasks, though
examining biases in creative tasks remains underexplored. Creative language
applications are meant for direct interaction with users, so it is important to
quantify and mitigate societal biases in these applications. We introduce a
novel study on a pipeline to mitigate societal biases when retrieving next
verse suggestions in a poetry composition system. Our results suggest that data
augmentation through sentiment style transfer has potential for mitigating
societal biases.
