Investigating Societal Biases in a Poetry Composition System

GeBNLP (COLING) 2020  ·  Emily Sheng, David Uthus ·

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.

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Datasets


Introduced in the Paper:

Gutenberg Poem Dataset

Results from the Paper


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Methods