Data Synthesis and Iterative Refinement for Neural Semantic Parsing without Annotated Logical Forms.
China National Conference on Chinese Computational Linguistics (CCL)(2022)
摘要
Semantic parsing aims to convert natural language utterances to logical forms. A critical challenge for constructing semantic parsers is the lack of labeled data. In this paper, we propose a data synthesis and iterative refinement framework for neural semantic parsing, which can build semantic parsers without annotated logical forms. We first generate a naive corpus by sampling logic forms from knowledge bases and synthesizing their canonical utterances. Then, we further propose a bootstrapping algorithm to iteratively refine data and model, via a denoising language model and knowledge-constrained decoding. Experimental results show that our approach achieves competitive performance on Geo, ATIS and Overnight datasets in both unsupervised and semi-supervised data settings.
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关键词
neural semantic parsing,iterative refinement,synthesis,data
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