  Linguistic entrainment is a phenomenon where people tend to mimic each other
in conversation. The core instrument to quantify entrainment is a linguistic
similarity measure between conversational partners. Most of the current
similarity measures are based on bag-of-words approaches that rely on
linguistic markers, ignoring the overall language structure and dialogue
context. To address this issue, we propose to use a neural network model to
perform the similarity measure for entrainment. Our model is context-aware, and
it further leverages a novel component to learn the shared high-level
linguistic features across dialogues. We first investigate the effectiveness of
our novel component. Then we use the model to perform similarity measure in a
corpus-based entrainment analysis. We observe promising results for both
evaluation tasks.
