  Slang is a common type of informal language, but its flexible nature and
paucity of data resources present challenges for existing natural language
systems. We take an initial step toward machine generation of slang by
developing a framework that models the speaker's word choice in slang context.
Our framework encodes novel slang meaning by relating the conventional and
slang senses of a word while incorporating syntactic and contextual knowledge
in slang usage. We construct the framework using a combination of probabilistic
inference and neural contrastive learning. We perform rigorous evaluations on
three slang dictionaries and show that our approach not only outperforms
state-of-the-art language models, but also better predicts the historical
emergence of slang word usages from 1960s to 2000s. We interpret the proposed
models and find that the contrastively learned semantic space is sensitive to
the similarities between slang and conventional senses of words. Our work
creates opportunities for the automated generation and interpretation of
informal language.
