  Semantic Pattern Similarity is an interesting, though not often encountered
NLP task where two sentences are compared not by their specific meaning, but by
their more abstract semantic pattern (e.g., preposition or frame). We utilize
Siamese Networks to model this task, and show its usefulness in determining SQL
patterns for unseen questions in a database-backed question answering scenario.
Our approach achieves high accuracy and contains a built-in proxy for
confidence, which can be used to keep precision arbitrarily high.
