  People naturally understand the emotions of-and often also empathize
with-those around them. In this paper, we predict the emotional valence of an
empathic listener over time as they listen to a speaker narrating a life story.
We use the dataset provided by the OMG-Empathy Prediction Challenge, a workshop
held in conjunction with IEEE FG 2019. We present a multimodal LSTM model with
feature-level fusion and local attention that predicts empathic responses from
audio, text, and visual features. Our best-performing model, which used only
the audio and text features, achieved a concordance correlation coefficient
(CCC) of 0.29 and 0.32 on the Validation set for the Generalized and
Personalized track respectively, and achieved a CCC of 0.14 and 0.14 on the
held-out Test set. We discuss the difficulties faced and the lessons learnt
tackling this challenge.
