  We describe a data-driven approach for automatically explaining new,
non-standard English expressions in a given sentence, building on a large
dataset that includes 15 years of crowdsourced examples from
UrbanDictionary.com. Unlike prior studies that focus on matching keywords from
a slang dictionary, we investigate the possibility of learning a neural
sequence-to-sequence model that generates explanations of unseen non-standard
English expressions given context. We propose a dual encoder approach---a
word-level encoder learns the representation of context, and a second
character-level encoder to learn the hidden representation of the target
non-standard expression. Our model can produce reasonable definitions of new
non-standard English expressions given their context with certain confidence.
