AdaNSP: Uncertainty-driven Adaptive Decoding in Neural Semantic Parsing

	AdaNSP: Uncertainty-driven Adaptive Decoding in Neural Semantic Parsing
	Zhang X(张翔)1,2 ; He SZ(何世柱)2 ; Liu K(刘康)1,2 ; Zhao J(赵军)1,2
	2019-07
会议名称	The 57th Annual Meeting of the Association for Computational Linguistics
会议录名称	Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
会议日期	2019-07
会议地点	Florence, Italy
摘要	Neural semantic parsers utilize the encoder-decoder framework to learn an end-to-end model for semantic parsing that transduces a natural language sentence to the formal semantic representation. To keep the model aware of the underlying grammar in target sequences, many constrained decoders were devised in a multi-stage paradigm, which decode to the sketches or abstract syntax trees first, and then decode to target semantic tokens. We instead to propose an adaptive decoding method to avoid such intermediate representations. The decoder is guided by model uncertainty and automatically uses deeper computations when necessary. Thus it can predict tokens adaptively. Our model outperforms the state-of-the-art neural models and does not need any expertise like predefined grammar or sketches in the meantime.
七大方向——子方向分类	自然语言处理
国重实验室规划方向分类	语音语言处理
是否有论文关联数据集需要存交	否
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/57624
专题	复杂系统认知与决策实验室
作者单位	1.University of Chinese Academy of Sciences, Beijing, 100049, China 2.National Laboratory of Pattern Recognition (NLPR), Institute of Automation Chinese Academy of Sciences, Beijing, 100190, China
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Zhang X,He SZ,Liu K,et al. AdaNSP: Uncertainty-driven Adaptive Decoding in Neural Semantic Parsing[C],2019.

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