This talk is part of the NLP Seminar Series.

Of Space Piracy and Secret Baby Romances: Deep Multi-View Book Representations and a Scalable Evaluation Framework

Lea Frermann, University of Edinburgh
Date: 11:00am - 12:00pm, Aug 24 2017
Venue: Room B12 Gates Computer Science Building (B12 is in the basement. Access is via elevator or from the right entrance of the building).


Automatically understanding the plot of novels is important both for informing literary scholarship and applications such as book summarization or recommendation. Humans select and recommend novels based on a variety of preferences (such as mood and types of featured characters, or their relations). We present a deep recurrent autoencoder model that learns richly structured multi-view plot representations from raw book text, approximating such preferences. While various models have addressed the task of story understanding, their evaluation has remained largely intrinsic and qualitative. We propose a principled and scalable framework leveraging expert-provided semantic tags (e.g., mystery, pirates) to evaluate plot representations in an extrinsic fashion, assessing their ability to produce locally coherent groupings of novels (micro-clusters) in model space. We show that our learnt multi-view representations yield better micro-clusters than less structured representations; and that they are interpretable, and thus useful for further literary analysis or labelling of the emerging micro-clusters.


Lea is a postdoc at the University of Edinburgh, and is currently a visiting scholar in the Language and Cognition lab at Stanford University. Previously she obtained a PhD from the University of Edinburgh, and interned at Amazon Machine Learning, Berlin. In her research she develops machine learning methods and computational models to gain a deeper understanding of the structure and dynamics of meaning representations both in language and in humans.