Memory-Based Semantic Parsing

Memory-Based Semantic Parsing Parag Jain and Mirella Lapata Institute for Language, Cognition and Computation School of Informatics, University of Edinburgh 10 Crichton Street, Edinburgh EH8 9AB, UK parag.jain@ed.ac.uk mlap@inf.ed.ac.uk Abstract We present a memory-based model for context- dependent semantic parsing. Previous approaches focus on enabling the decoder to copy or mod- ify the parse from the previous utterance, as- suming 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 se- quential user utterances. We evaluate our ap- proach on three semantic parsing benchmarks. Experimental results show that our model can better process context-dependent information and demonstrates improved performance with- out using task-specific decoders. 1 Introduction Semantic parsing is the task of converting natu- ral language utterances into machine interpreta- ble meaning representations such as executable queries or logical forms. It has emerged as an important component in many natural language interfaces ( ˝Ozcan et al., 2020) with applications in robotics (Dukes, 2014), question answering (Zhong et al., 2018; Yu et al., 2018b), dialogue systems (Artzi and Zettlemoyer, 2011), and the Internet of Things (Campagna et al., 2017). Neural network based approaches have led to significant improvements in semantic pars- ing (Zhong et al., 2018; Kamath and Das, 2019; Yu et al., 2018b; Yavuz et al., 2018; Yu et al., 2018a) across domains and semantic formalisms. The majority of existing studies focus on pars- ing utterances in isolation, and as a result they cannot readily transfer in more realistic settings where users ask multiple inter-related questions to satisfy an information need. In this work, we study context-dependent semantic parsing focus- ing specifically on text-to-SQL generation, which has emerged as a popular application area in recent years. Figure 1 shows a sequence of utterances in an interaction. The discourse focuses on a specific topic serving a specific information need, namely, finding out which Continental flights leave from Chicago on a given date and time. Importantly, interpreting each of these utterances, and mapping them to a database query to retrieve an answer, needs to be situated in a particular context as the exchange proceeds. The topic further evolves as the discourse transitions from one utterance to the next and constraints (e.g., TIME or PLACE) are added or revised. For example, in Q2 the TIME constraint before 10am from Q1 is revised to before noon, and in Q3 to before 2pm. Aside from such topic extensions (Chai and Jin, 2004), the interpretation of Q2 and Q3 depends on Q1, as it is implied that the questions concern Continental flights that go from Chicago to Seattle, not just any Continental flights, however the phrase from Chicago to Seattle is elided from Q2 and Q3. The interpretation of Q4 depends on Q3, which in turn depends on Q1. Interestingly, Q5 introduces information with no dependencies on previous discourse and, in this case, relying on information from …

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