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Data Analysis and Knowledge Discovery  2023, Vol. 7 Issue (12): 1-21    DOI: 10.11925/infotech.2096-3467.2022.1074
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Review of Text Neural Semantic Parsing Methods
Shen Lingyun1,2,Le Xiaoqiu1,2()
1National Science Library, Chinese Academy of Sciences, Beijing 100190, China
2Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
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Abstract  

[Objective] This paper summarizes and comments on the research methods of text semantic parsing with neural networks in the past decade. [Coverage] With Google Scholar and CNKI as the data retrieval platforms, and “Neural Semantic Parsing” as the keywords, all relevant papers and their important citations from 2010 to 2022 were retrieved for analysis. [Methods] The paper classified the existing neural semantic parsing methods according to the technical path, explained the basic ideas of each technical path, compared and analyzed the similarities and differences of each technology method in data, performance, application goals, etc., and summarized the existing problems and development tendency of text neural semantic parsing technology. [Results] Neural semantic parsing methods could be summarized into three types, sequence to sequence method, intermediate form based method, and semantic unit decomposition and combination method. The latter two methods are improvements to the first method. At present, intermediate representations such as semantic sketch, canonical utterance and few-shot neural semantic parsing are the main research focuses. [Limitations] The paper mainly summarized and analyzed the existing research ideas from the methodology, but does not elaborate the internal implementation mechanism of the neural semantic parsing models. [Conclusions] The neural semantic parsing method gains the best performance in text semantic parsing at present. The current popular practice is to design targeted neural network models for specific applications. But the effect of semantic parsing is still far from the practical application.
Key words： Semantic Parsing      Neural Network Model      Semantic Representation      Pre-training     
Received: 13 October 2022      Published: 02 February 2024
ZTFLH:  G350  
Corresponding Authors: Le Xiaoqiu，ORCID：0000-0002-7114-5544，E-mail：lexq@mail.las.ac.cn。   
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Lingyun Shen
Xiaoqiu Le

Cite this article:

Shen Lingyun, Le Xiaoqiu. Review of Text Neural Semantic Parsing Methods. Data Analysis and Knowledge Discovery, 2023, 7(12): 1-21.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.1074     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2023/V7/I12/1

Basic Components of Neural Semantic Parsing
场景 自然语言语句 语义形式化表示
数学问题解算[8] Dan have 2 pens，Jessica have 4 pens. How many pens do they have in total ? x = 4+2
逻辑查询[5] what microsoft jobs do not require a bscs? answer（J，（company（J，'microsoft'），job（J），not（（req_deg（J，'bscs'）））））
问答[63] who has published the most articles? argmax（type.person；R（x：count（type.article u author：x）））
程序生成[4] Adds a scalar to this vector in place public void add（final double arg0）{
for （int i = 0；i < vecElements.length（）；i++）{
vecElements[i] += arg0；}}
机器人指令生成[64] Go away from the lamp to the intersection of the red brick and wood Turn（），
Travel（steps：1）
Semantic Representations Required for Different Semantic Resolution Task
Process of Different Neural Semantic Analysis Methods
Decoder Decoding Process with Attention Mechanism
Comparison Between Sketch Based Semantic Parsing and End-to-End Semantic Parsing
Three Strategies of Pre-training Tuning for Semantic Parsing Tasks
方法类型 文献 模型结构 语义解析
目标		 方法特点
序列到序列
的方法		 Mei等[28] LSTM编码器-解码器 导航序列 多层对齐机制；准确度超过机器学习算法
Wang等[8] 编码器GRU-解码器LSTM架构 数学方程式 首次使用神经网络解决数学语义解析问题；需要相似度检索
Ko?isky等[72] 三层LSTM编码层-LSTM解码层-Attention 知识库查询 随机生成伪数据的半监督方法
Babu等[30] 基于CNN的编码器-解码器架构-多头注意力 知识库查询 并行解码，计算速度快
Ling等[31] 指针网络 程序语言 使用指针网络解码器，性能超过Dong等[5]注意力的方法
Rongali等[35] BERT编码器-Transformers解码器 逻辑查询 性能首次超过人类水平的语义解析
Chen等[56] BART编码器-Transformers解码器 语义框架 改进了BART在语义解析任务上的效果
Wang等[75] BiLSTM编码器-LSTM-Attention解码器 数学方程式 使用预设方程模板约束解码，优化了输出方程的格式
Bogin等[78] GNN-LSTM编码器+解码器 数据表查询 将数据库结构和文本共同输入编码器，增加编码和解码过程结构信息决策
Wang等[81] BiLSTM-Self Attention SQL 改进了Bogin等[78]的工作，增加文本和表、列名的链接
Yavuz等[83] LSTM编码器-解码器-实体类型识别函数 知识库问答 在RNN序列到序列模型基础上，额外增加识别答案实体类型
借助中间形式
的方法		 Dong等[87] 共享BiLSTM编码器-粗略意义解码器；BiLSTM+Attention-逻辑形式生成解码器RNN-Attention λ-演算式 首次提出将语义解析分为两阶段解决的问题，在跨领域任务上优势明显
Nye等[86] 草图生成器：RNN-Attention；符号推理合成器 程序语言 手工设计介于自然语言和程序之间的中间草图，用RNN生成符号系统
Xu等[89] BiLSTM-列Attention SQL 设计了通用的草图模板，较所有直接生成的方法准确度提升9%~13%
Shin等[94] GPT3-Prompt λ-演算式 通过微调预训练模型生成释义
基于分解与
组合的方法		 Zhong等[32] 指针网络-强化学习 SQL 模型内部分别使用注意力生成对应逻辑式从句，较直接生成的方法准确度提高近20%
Lindemann等[97] 分解算法-BERT AMR 适用于跨图语言的语义解析任务
Li等[99] 移进-规约算法-Bi-GRU解码 逻辑形式 算法和模型简单，精度高
Representation Methods of Neural Semantic Analysis
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