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We consider the problem of embedding character-entity relationships from thereduced semantic space of narratives, proposing and evaluating the assumptionthat these relationships hold under a reflection operation. We analyze thisassumption and compare the approach to a baseline state-of-the-art model with aunique evaluation that simulates efficacy on a downstream clustering task withhuman-created labels. Although our model creates clusters that achieveSilhouette scores of -.084, outperforming the baseline -.227, our analysisreveals that the models approach the task much differently and perform well onvery different examples. We conclude that our assumption might be useful forspecific types of data and should be evaluated on a wider range of tasks.

Improving Narrative Relationship Embeddings by Training with Additional Inverse-Relationship Constraints