TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
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DOI：
10.18653/v1/d18-2002
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发表时间：
2018-10
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通讯作者：
Pengcheng Yin;Graham Neubig
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作者：
Pengcheng Yin;Graham Neubig
我们提出了基于过渡的神经语义解析器Tranx，将自然语言（NL）映射到形式含义表示（MRS）中。 Tranx使用基于Ampact Syntax描述目标MR的过渡系统，这给出了两个主要优点：（1）使用目标MR的语法中的信息来限制输出空间并建模信息，这是高度准确的。流量，（2）它是高度概括的，可以通过仅编写与MR中允许结构相对应的新的抽象语法描述来轻松地应用于新的MR类型。与现有的神经语义解析器相比，对四个不同语义解析和代码生成任务进行了四个不同的语义解析和代码生成任务的实验表明，可以概括地记录结果。
We present TRANX, a transition-based neural semantic parser that maps natural language (NL) utterances into formal meaning representations (MRs). TRANX uses a transition system based on the abstract syntax description language for the target MR, which gives it two major advantages: (1) it is highly accurate, using information from the syntax of the target MR to constrain the output space and model the information flow, and (2) it is highly generalizable, and can easily be applied to new types of MR by just writing a new abstract syntax description corresponding to the allowable structures in the MR. Experiments on four different semantic parsing and code generation tasks show that our system is generalizable, extensible, and effective, registering strong results compared to existing neural semantic parsers.