Memory-Based Semantic Parsing
This work addresses context-dependent semantic parsing for natural language processing applications, presenting an incremental improvement over previous methods.
The paper tackles the problem of context-dependent semantic parsing by introducing a memory-based model that uses an external memory to represent contextual information, achieving improved performance on three benchmarks without task-specific decoders.
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.