  In this paper, we present a novel approach to natural language understanding
that utilizes context-free grammars (CFGs) in conjunction with
sequence-to-sequence (seq2seq) deep learning. Specifically, we take a CFG
authored to generate dialogue for our target application for NLU, a videogame,
and train a long short-term memory (LSTM) recurrent neural network (RNN) to map
the surface utterances that it produces to traces of the grammatical expansions
that yielded them. Critically, this CFG was authored using a tool we have
developed that supports arbitrary annotation of the nonterminal symbols in the
grammar. Because we already annotated the symbols in this grammar for the
semantic and pragmatic considerations that our game's dialogue manager operates
over, we can use the grammatical trace associated with any surface utterance to
infer such information. During gameplay, we translate player utterances into
grammatical traces (using our RNN), collect the mark-up attributed to the
symbols included in that trace, and pass this information to the dialogue
manager, which updates the conversation state accordingly. From an offline
evaluation task, we demonstrate that our trained RNN translates surface
utterances to grammatical traces with great accuracy. To our knowledge, this is
the first usage of seq2seq learning for conversational agents (our game's
characters) who explicitly reason over semantic and pragmatic considerations.
