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We describe a data-driven approach for automatically explaining new,non-standard English expressions in a given sentence, building on a largedataset that includes 15 years of crowdsourced examples fromUrbanDictionary.com. Unlike prior studies that focus on matching keywords froma slang dictionary, we investigate the possibility of learning a neuralsequence-to-sequence model that generates explanations of unseen non-standardEnglish expressions given context. We propose a dual encoder approach---aword-level encoder learns the representation of context, and a secondcharacter-level encoder to learn the hidden representation of the targetnon-standard expression. Our model can produce reasonable definitions of newnon-standard English expressions given their context with certain confidence.

Learning to Explain Non-Standard English Words and Phrases