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      Practical Semantic Parsing for Spoken Language Understanding

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          Abstract

          Executable semantic parsing is the task of converting natural language utterances into logical forms that can be directly used as queries to get a response. We build a transfer learning framework for executable semantic parsing. We show that the framework is effective for Question Answering (Q&A) as well as for Spoken Language Understanding (SLU). We further investigate the case where a parser on a new domain can be learned by exploiting data on other domains, either via multi-task learning between the target domain and an auxiliary domain or via pre-training on the auxiliary domain and fine-tuning on the target domain. With either flavor of transfer learning, we are able to improve performance on most domains; we experiment with public data sets such as Overnight and NLmaps as well as with commercial SLU data. We report the first parsing results on Overnight and state-of-the-art results on NLmaps. The experiments carried out on data sets that are different in nature show how executable semantic parsing can unify different areas of NLP such as Q&A and SLU.

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          Most cited references 10

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              Algorithms for Deterministic Incremental Dependency Parsing

               Joakim Nivre (2008)
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                Author and article information

                Journal
                11 March 2019
                2019-03-13
                Article
                1903.04521

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                Proceedings of NAACL 2019
                cs.CL

                Theoretical computer science

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