  We present a memory-based model for context-dependent semantic parsing.
Previous approaches focus on enabling the decoder to copy or modify the parse
from the previous utterance, assuming there is a dependency between the current
and previous parses. In this work, we propose to represent contextual
information using an external memory. We learn a context memory controller that
manages the memory by maintaining the cumulative meaning of sequential user
utterances. We evaluate our approach on three semantic parsing benchmarks.
Experimental results show that our model can better process context-dependent
information and demonstrates improved performance without using task-specific
decoders.
