  Slang is a predominant form of informal language making flexible and extended
use of words that is notoriously hard for natural language processing systems
to interpret. Existing approaches to slang interpretation tend to rely on
context but ignore semantic extensions common in slang word usage. We propose a
semantically informed slang interpretation (SSI) framework that considers
jointly the contextual and semantic appropriateness of a candidate
interpretation for a query slang. We perform rigorous evaluation on two
large-scale online slang dictionaries and show that our approach not only
achieves state-of-the-art accuracy for slang interpretation in English, but
also does so in zero-shot and few-shot scenarios where training data is sparse.
Furthermore, we show how the same framework can be applied to enhancing machine
translation of slang from English to other languages. Our work creates
opportunities for the automated interpretation and translation of informal
language.
