2016_naacl_relationships.tex

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\title{Feuding Families and Former Friends:\\Unsupervised Learning for Dynamic Fictional Relationships}

\author{
  Mohit Iyyer,$^{1}$ Anupam Guha,$^{1}$ Snigdha Chaturvedi,$^{1}$ Jordan Boyd-Graber,$^{2}$ Hal Daumé III$^{1}$\\
  $^1$University of Maryland, Department of Computer Science and \abr{umiacs}\\
        $^2$University of Colorado, Department of Computer Science \\
  {\tt \{miyyer,aguha,snigdac,hal\}@umiacs.umd.edu}, \\
  {\tt Jordan.Boyd.Graber@colorado.edu} \\
}

\begin{document}

\maketitle

\begin{abstract}
  Understanding how a fictional relationship between two characters changes over
  time (e.g., from best friends to sworn enemies) is a key challenge in digital
  humanities scholarship.  We present a novel unsupervised neural network for
  this task that incorporates dictionary learning to generate interpretable,
  accurate relationship trajectories. While previous work on characterizing
  literary relationships relies on plot summaries annotated with predefined
  labels, our model jointly learns a set of global relationship descriptors as
  well as a trajectory over these descriptors for each relationship in a dataset
  of raw text from novels. We find that our model learns descriptors of events
  (e.g., marriage or murder) as well as interpersonal states (love,
  sadness). Our model outperforms topic model baselines on two crowdsourced
  tasks, and we also find interesting correlations to annotations in an existing
  dataset.
\end{abstract}

\input{2016_naacl_relationships/sections/introduction}
\input{2016_naacl_relationships/sections/data}
\input{2016_naacl_relationships/sections/model}
\input{2016_naacl_relationships/sections/experiments}
\input{2016_naacl_relationships/sections/discussion}
\input{2016_naacl_relationships/sections/related}
\input{2016_naacl_relationships/sections/conclusion}

\section*{Acknowledgments}

We thank Jonathan Chang and Amit Gruber for providing baseline code, Thang Nguyen for helpful discussions about our model, and the anonymous reviewers for their insightful comments.
This work was supported by \abr{nsf} grant \abr{iis}-1320538.
Boyd-Graber is also partially supported by \abr{nsf} grants
\abr{ccf}-1409287 and \abr{ncse}-1422492. Any opinions, findings,
conclusions, or recommendations expressed here are those of the
authors and do not necessarily reflect the view of the sponsor.

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