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
[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.
Kamath A, Das R. A Survey on Semantic Parsing[OL]. arXiv Preprint, arXiv:1812.00978.
[2]
Lee C, Gottschlich J, Roth D. A Survey on Semantic Parsing for Machine Programming[C]// Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2021.
[3]
Yang J F, Jiang H M, Yin Q Y, et al. SEQZERO: Few-Shot Compositional Semantic Parsing with Sequential Prompts and Zero-Shot Models[C]// Findings of the Association for Computational Linguistics:NAACL 2022. 2022: 49-60.
[4]
Iyer S, Cheung A, Zettlemoyer L. Learning Programmatic Idioms for Scalable Semantic Parsing[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. 2019: 5426-5435.
[5]
Dong L, Lapata M. Language to Logical Form with Neural Attention[C]// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1:Long Papers). 2016: 33-43.
[6]
Krishnamurthy J, Dasigi P, Gardner M. Neural Semantic Parsing with Type Constraints for Semi-Structured Tables[C]// Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017: 1516-1526.
[7]
Bengio Y, Ducharme R, Vincent P, et al. A Neural Probabilistic Language Model[J]. The Journal of Machine Learning Research, 2000, 3: 1137-1155.
[8]
Wang Y, Liu X J, Shi S M. Deep Neural Solver for Math Word Problems[C]// Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017: 845-854.
[9]
Mikolov T, Chen K, Corrado G. et al. Efficient Estimation of Word Representations in Vector Space[OL]. arXiv Preprint, arXiv:1301.3781.
[10]
Pennington J, Socher R, Manning C. GloVe: Global Vectors for Word Representation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. 2014: 1532-1543.
[11]
Peters M E, Neumann M, Iyyer M, et al. Deep Contextualized Word Representations[C]// Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. 2018, 1: 2227-2237.
[12]
Radford A, Narasimhan K, Salimans T, et al. Improving Language Understanding by Generative Pre-training[R]. OpenAI Technical Report, 2018.
[13]
Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[C]// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies, Volume 1. 2018: 4171-4186.
[14]
Liu Y H, Ott M, Goyal N, et al. RoBERTa: A Robustly Optimized Bert Pretraining Approach[OL]. arXiv Preprint, arXiv:1907.11692.
[15]
Lewis M, Liu Y H, Goyal N, et al. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension[OL]. arXiv Preprint, arXiv:1910.13461.
[16]
Le Q, Mikolov T. Distributed Representations of Sentences and Documents[C]// Proceedings of the 31st International Conference on Machine Learning. 2014.
[17]
Dai A M, Olah C, Le Q V. Document Embedding with Paragraph Vectors[OL]. arXiv Preprint, arXiv:1507.07998.
[18]
Zhang M H, Wu Y F, Li W K, et al. Learning Universal Sentence Representations with Mean-Max Attention Autoencoder[C]// Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018: 4514-4523.
[19]
Wu L F, Yen I E H, Xu K, et al. Word Mover’s Embedding: From Word2Vec to Document Embedding[C]// Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018: 4524-4534.
[20]
Vinyals O, Fortunato M, Jaitly N. Pointer Networks[OL]. arXiv Preprint, arXiv:1506.03134.
[21]
Radford A, Wu J, Child R, et al. Language Models Are Unsupervised Multitask Learners[R]. OpenAI Blog, 2019.
[22]
Brown T B, Mann B, Ryder N, et al. Language Models Are Few-Shot Learners[C]// Proceedings of the 34th International Conference on Neural Information Processing Systems. 2020: 1877-1901.
[23]
Raffel C, Shazeer N, Roberts A, et al. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer[J]. The Journal of Machine Learning Research, 2020, 21(1): 5485-5551.
[24]
Das D, Schneider N, Chen D S, et al. Probabilistic Frame-Semantic Parsing[C]// Proceedings of the 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. 2010: 948-956.
[25]
Grefenstette E, Blunsom P, de Freitas N, et al. A Deep Architecture for Semantic Parsing[C]// Proceedings of the ACL 2014 Workshop on Semantic Parsing. 2014: 22-27.
(Kong Lingfu, Gao Shengnan, Wu Peiliang. An Indoor Service System for Parsing of Deep Information from Chinese Voice Commands[J]. Chinese High Technology Letters, 2014, 24(11): 1101-1107.)
[27]
Jia R, Liang P. Data Recombination for Neural Semantic Parsing[OL]. arXiv Preprint, arXiv:1606.03622.
[28]
Mei H Y, Bansal M, Walter M R. Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences[C]// Proceedings of the 30th AAAI Conference on Artificial Intelligence. 2016: 2772-2778.
[29]
Yih W T, He X D, Meek C. Semantic Parsing for Single-Relation Question Answering[C]// Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2:Short Papers). 2014: 643-648.
[30]
Babu A R, Shrivastava A, Aghajanyan A, et al. Non-autoregressive Semantic Parsing for Compositional Task-Oriented Dialog[C]// Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. 2021: 2969-2978.
[31]
Ling W, Grefenstette E, Hermann K M, et al. Latent Predictor Networks for Code Generation[OL]. arXiv Preprint, arXiv:1603.06744.
[32]
Zhong V, Xiong C M, Socher R. Seq2SQL: Generating Structured Queries from Natural Language Using Reinforcement Learning[OL]. arXiv Preprint, arXiv:1709.00103.
[33]
Vaswani A, Shazeer N, Parmar N, et al. Attention is All You Need[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017: 6000-6010.
[34]
Hwang W, Yim J, Park S, et al. A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization[OL]. arXiv Preprint, arXiv:1902.01069.
[35]
Rongali S, Soldaini L, Monti E, et al. Don’t Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing[C]// Proceedings of the Web Conference 2020. 2020: 2962-2968.
[36]
Lyu Q, Chakrabarti K, Hathi S, et al. Hybrid Ranking Network for Text-to-SQL[OL]. arXiv Preprint, arXiv:2008.04759.
[37]
Lee C, Gottschlich J, Roth D. Toward Code Generation: A Survey and Lessons from Semantic Parsing[OL]. arXiv Preprint, arXiv:2105.03317.
[38]
Cresswell M J. Formal Philosophy, Selected Papers of Richard Montague[J]. Philosophia, 1976, 6: 193-207.
doi: 10.1007/BF02383265
[39]
Carpenter B. Type-Logical Semantics[M]. Cambridge: MIT Press, 1998.
[40]
Getoor L, Taskar B. Markov Logic: A Unifying Framework for Statistical Relational Learning[M]. Cambridge: MIT Press, 2007.
[41]
Enderton H B. A Mathematical Introduction to Logic[M]. Amsterdam: Elsevier, 2001.
[42]
Poon H, Domingos P. Unsupervised Semantic Parsing[C]// Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1. 2009: 1-10.
[43]
Artzi Y, Zettlemoyer L. Bootstrapping Semantic Parsers from Conversations[C]// Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. 2011: 421-432.
[44]
Krishnamurthy J, Mitchell T M. Weakly Supervised Training of Semantic Parsers[C]// Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 2012: 754-765.
[45]
Liang P. Lambda Dependency-Based Compositional Semantics[OL]. arXiv Preprint, arXiv:1309.4408.
[46]
Kwiatkowski T, Zettlemoyer L, Goldwater S, et al. Inducing Probabilistic CCG Grammars from Logical Form with Higher-Order Unification[C]// Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. 2010: 1223-1233.
[47]
Wang C, Xue N W, Pradhan S. A Transition-Based Algorithm for AMR Parsing[C]// Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. 2015: 366-375.
[48]
Flanigan J, Thomson S, Carbonell J, et al. A Discriminative Graph-Based Parser for the Abstract Meaning Representation[C]// Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. 2014: 1426-1436.
[49]
Konstas I, Iyer S, Yatskar M. et al. Neural AMR: Sequence-to-Sequence Models for Parsing and Generation[OL]. arXiv Preprint, arXiv:1704.08381.
[50]
Hershcovich D, Abend O, Rappoport A. A Transition-Based Directed Acyclic Graph Parser for UCCA[OL]. arXiv Preprint, arXiv:1704.00552.
[51]
Shi S M, Wang Y, Lin C Y, et al. Automatically Solving Number Word Problems by Semantic Parsing and Reasoning[C]// Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2015: 1132-1142.
[52]
Fillmore C J, Johnson C R, Petruck M R L. Background to FrameNet[J]. International Journal of Lexicography, 2003, 16(3): 235-250.
doi: 10.1093/ijl/16.3.235
[53]
Fillmore C J. Chapter 10 Frame Semantics[A]//Cognitive Linguistics: Basic Readings[M]. Berlin: de Gruyter Mouton, 2006: 373-400.
[54]
Fillmore C J, Baker C. A Frames Approach to Semantic Analysis[A]//The Oxford Handbook of Linguistic Analysis[M]. New York: Oxford University Press, 2012: 313-339.
[55]
Gupta S, Shah R, Mohit M. et al. Semantic Parsing for Task Oriented Dialog Using Hierarchical Representations[OL]. arXiv Preprint, arXiv:1810.07942.
[56]
Chen X L, Ghoshal A, Mehdad Y, et al. Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing[C]// Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. 2020: 5090-5100.
[57]
Desai S, Aly A. Diagnosing Transformers in Task-Oriented Semantic Parsing[C]// Findings of the Association for Computational Linguistics:ACL-IJCNLP 2021. 2021: 57-62.
[58]
Liang P. Learning Executable Semantic Parsers for Natural Language Understanding[J]. Communications of the ACM, 2016, 59(9): 68-76.
[59]
Banarescu L, Bonial C, Cai S, et al. Abstract Meaning Representation for Sembanking[C]// Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse. 2013: 178-186.
[60]
Abend O, Rappoport A. Universal Conceptual Cognitive Annotation (UCCA)[C]// Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. 2013: 228-238.
[61]
White A S, Reisinger D, Sakaguchi K, et al. Universal Decompositional Semantics on Universal Dependencies[C]// Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2016: 1713-1723.
[62]
Abend O, Rappoport A. The State of the Art in Semantic Representation[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. 2017: 77-89.
[63]
Wang Y S, Berant J, Liang P. Building a Semantic Parser Overnight[C]// Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. 2015: 1332-1342.
[64]
Chen D L, Mooney R J. Learning to Interpret Natural Language Navigation Instructions from Observations[C]// Proceedings of the 25th AAAI Conference on Artificial Intelligence. 2011: 859-865.
[65]
Sutskever I, Vinyals O, Le Q V. Sequence to Sequence Learning with Neural Networks[OL]. arXiv Preprint, arXiv:1409.3215.
[66]
Zettlemoyer L S, Collins M. Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars[OL]. arXiv Preprint, arXiv:1207.1420.
(Wang Xinlei, Li Shuaichi, Yang Zhihao, et al. Chinese Knowledge Base Question Answering System Based on Pre-trained Language Model[J]. Journal of Shanxi University(Natural Science Edition), 2020, 43(4): 955-962.)
(Fan Hongjie, Li Xuedong, Ye Songtao. Aided Disease Diagnosis Method for EMR Semantic Analysis[J]. Computer Science, 2022, 49(1): 153-158.)
doi: 10.11896/jsjkx.201100125
[71]
Zhang Z Y, Han X, Liu Z Y, et al. ERNIE: Enhanced Language Representation with Informative Entities[C]// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019: 1441-1451.
[72]
Kočiský T, Melis G, Grefenstette E, et al. Semantic Parsing with Semi-supervised Sequential Autoencoders[C]// Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2016: 1078-1087.
[73]
Shao B, Gong Y Y, Bao J W, et al. Weakly Supervised Multi-task Learning for Semantic Parsing[C]// Proceedings of the 28th International Joint Conference on Artificial Intelligence. 2019: 3375-3381.
[74]
Bahdanau D, Cho K, Bengio Y. Neural Machine Translation by Jointly Learning to Align and Translate[OL]. arXiv Preprint, arXiv:1409.0473.
[75]
Wang L, Zhang D X, Zhang J P, et al. Template-Based Math Word Problem Solvers with Recursive Neural Networks[C]// Proceedings of the 33rd AAAI Conference on Artificial Intelligence and the 31st Innovative Applications of Artificial Intelligence Conference and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence. 2019: 7144-7151.
[76]
Huang D Q, Liu J, Lin C Y, et al. Neural Math Word Problem Solver with Reinforcement Learning[C]// Proceedings of the 27th International Conference on Computational Linguistics. 2018: 213-223.
[77]
Yin P C, Neubig G. A Syntactic Neural Model for General-Purpose Code Generation[OL]. arXiv Preprint, arXiv:1704.01696.
[78]
Bogin B, Berant J, Gardner M. Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing[C]// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019: 4560-4565.
[79]
Li Y J, Tarlow D, Brockschmidt M, et al. Gated Graph Sequence Neural Networks[OL]. arXiv Preprint, arXiv:1511.05493.
[80]
Sorokin D, Gurevych I. Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering[C]// Proceedings of the 27th International Conference on Computational Linguistics. 2018: 3306-3317.
[81]
Wang B L, Shin R, Liu X D, et al. RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020: 7567-7578.
[82]
Yu T, Zhang R, Yang K, et al. Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task[C]// Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018: 3911-3921.
[83]
Yavuz S, Gur I, Su Y, et al. Improving Semantic Parsing via Answer Type Inference[C]// Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2016: 149-159.
[84]
Shaw P, Massey P, Chen A, et al. Generating Logical Forms from Graph Representations of Text and Entities[OL]. arXiv Preprint, arXiv:1905.08407.
[85]
Solar-Lezama A, Tancau L, Bodik R, et al. Combinatorial Sketching for Finite Programs[C]// Proceedings of the 12th International Conference on Architectural Support for Programming Languages and Operating Systems. 2006: 404-415.
[86]
Nye M, Hewitt L, Tenenbaum J, et al. Learning to Infer Program Sketches[C]// Proceedings of the 36th International Conference on Machine Learning. 2019.
[87]
Dong L, Lapata M. Coarse-to-Fine Decoding for Neural Semantic Parsing[OL]. arXiv Preprint, arXiv:1805.04793.
(Zhao Ruizhuo, Gao Jinhua, Sun Xiaoqian, et al. Learning Tree-Structured Sketch for Neural Semantic Parsing[J]. Journal of Chinese Information Processing, 2021, 35(1): 9-16.)
[89]
Xu X J, Liu C, Song D. SQLNet: Generating Structured Queries from Natural Language without Reinforcement Learning[OL]. arXiv Preprint, arXiv: 1711.04436.
[90]
Li Z C, Lai Y X, Xie Y X, et al. A Sketch-Based System for Semantic Parsing[C]// Proceedings of the CCF International Conference on Natural Language Processing and Chinese Computing. 2019: 748-759.
[91]
Ye X, Chen Q C, Wang X Y, et al. Sketch-Driven Regular Expression Generation from Natural Language and Examples[J]. Transactions of the Association for Computational Linguistics, 2020, 8: 679-694.
doi: 10.1162/tacl_a_00339
[92]
Berant J, Liang P. Semantic Parsing via Paraphrasing[C]// Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. 2014: 1415-1425.
[93]
Berant J, Chou A, Frostig R, et al. Semantic Parsing on Freebase from Question-Answer Pairs[C]// Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. 2013: 1533-1544.
[94]
Shin R, Lin C H, Thomson S, et al. Constrained Language Models Yield Few-Shot Semantic Parsers[OL]. arXiv Preprint, arXiv: 2104.08768.
(Li Qing, Zhong Jiang, Li Lili, et al. Self-Correcting Complex Semantic Analysis Method Based on Pre-training Mechanism[J]. Journal on Communications, 2019, 40(12): 41-50.)
doi: 10.11959/j.issn.1000-436x.2019195
(Li Qing, Zhong Jiang, Li Lili, et al. Semantic Parsing Graph Query Model for On-demand Aggregation[J]. Acta Electronica Sinica, 2020, 48(4): 763-771.)
doi: 10.3969/j.issn.0372-2112.2020.04.018
[97]
Lindemann M, Groschwitz J, Koller A. Compositional Semantic Parsing Across Graphbanks[C]// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019: 4576-4585.
[98]
Groschwitz J, Lindemann M, Fowlie M. et al. AMR Dependency Parsing with a Typed Semantic Algebra[OL]. arXiv Preprint, arXiv: 1805.11465.
[99]
Li Y T, Chen B, Liu Q, et al. Keep the Structure: A Latent Shift-Reduce Parser for Semantic Parsing[C]// Proceedings of the 30th International Joint Conference on Artificial Intelligence. 2021: 3864-3870.
[100]
Han X, Zhang Z Y, Ding N, et al. Pre-trained Models: Past, Present and Future[J]. AI Open, 2021, 2: 225-250.
doi: 10.1016/j.aiopen.2021.08.002
[101]
Liang Z W, Zhang J P, Shao J, et al. MWP-BERT: A Strong Baseline for Math Word Problems[OL]. arXiv Preprint, arXiv: 2107.13435.
[102]
Xu S L, Semnani S J, Campagna G, et al. AutoQA: From Databases to QA Semantic Parsers with Only Synthetic Training Data[OL]. arXiv Preprint, arXiv: 2010.04806.
[103]
Wu S, Chen B, Xin C L, et al. From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding[OL]. arXiv Preprint, arXiv: 2106.06228.
[104]
Rongali S, Arkoudas K, Rubino M, et al. Training Naturalized Semantic Parsers with Very Little Data[OL]. arXiv Preprint, arXiv: 2204.14243.
[105]
Schucher N, Reddy S, de Vries H. The Power of Prompt Tuning for Low-Resource Semantic Parsing[OL]. arXiv Preprint, arXiv: 2110.08525.
[106]
Shin R, van Durme B. Few-Shot Semantic Parsing with Language Models Trained on Code[OL]. arXiv Preprint, arXiv: 2112.08696.
[107]
Sun W Q, Khan H, des Mesnards N G, et al. Unfreeze with Care: Space-Efficient Fine-Tuning of Semantic Parsing Models[C]// Proceedings of the ACM Web Conference 2022. 2022: 999-1007.
[108]
Li X L, Liang P. Prefix-Tuning: Optimizing Continuous Prompts for Generation[C]// Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1:Long Papers). 2021: 4582-4597.
[109]
Zaken E B, Ravfogel S, Goldberg Y. BitFit: Simple Parameter-Efficient Fine-Tuning for Transformer-Based Masked Language-Models[C]// Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2:Short Papers). 2021: 1-9.
[110]
Guo J Q, Liu Q, Lou J G, et al. Benchmarking Meaning Representations in Neural Semantic Parsing[C]// Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. 2020: 1520-1540.
[111]
Sun Y B, Tang D Y, Duan N, et al. Semantic Parsing with Syntax-and Table-Aware SQL Generation[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. 2018: 361-372.
[112]
Iyer S, Konstas I, Cheung A, et al. Learning a Neural Semantic Parser from User Feedback[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. 2017: 963-973.
(Zuo Min, Xu Zelong, Zhang Qingchuan, et al. A Question Answering Model of Food Domain Knowledge Bases with Two-Dimension Chinese Semantic Analysis[J]. Journal of Zhengzhou University (Engineering Science), 2020, 41(3): 8-13.)
[114]
Bobrow D G. Natural Language Input for a Computer Problem Solving System[M]. Cambridge: Massachusetts Institute of Technology, 1964.
[115]
Charniak E. Computer Solution of Calculus Word Problems[C]// Proceedings of the 1st International Joint Conference on Artificial Intelligence. 1969: 303-316.
[116]
Hosseini M J, Hajishirzi H, Etzioni O, et al. Learning to Solve Arithmetic Word Problems with Verb Categorization[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. 2014: 523-533.
[117]
Kushman N, Artzi Y, Zettlemoyer L, et al. Learning to Automatically Solve Algebra Word Problems[C]// Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. 2014: 271-281.
[118]
Zelle J M, Mooney R J. Learning to Parse Database Queries Using Inductive Logic Programming[C]// Proceedings of the 13th National Conference on Artificial Intelligence - Volume 2. 1996: 1050-1055.
[119]
Quirk C, Mooney R, Galley M. Language to Code: Learning Semantic Parsers for If-This-Then-That Recipes[C]// Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. 2015: 878-888.
[120]
Yao Z Y, Li X J, Gao J F, et al. Interactive Semantic Parsing for If-Then Recipes Via Hierarchical Reinforcement Learning[C]// Proceedings of the 33rd AAAI Conference on Artificial Intelligence. 2019: 2547-2554.
[121]
Chen X Y, Liu C, Shin R, et al. Latent Attention for If-Then Program Synthesis[C]// Proceedings of the 30th International Conference on Neural Information Processing Systems. 2016: 4581-4589.
[122]
Rabinovich M, Stern M, Klein D. Abstract Syntax Networks for Code Generation and Semantic Parsing[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. 2017: 1139-1149.
[123]
Aho A V, Lam M S, Sethi R, et al. Compilers:Principles, Techniques, & Tools[M]. The 2nd Edtion. Boston: Pearson Addison-Wesley, 2007.
[124]
Yin P C, Neubig G. A Syntactic Neural Model for General-Purpose Code Generation[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. 2017: 440-450.
[125]
Yin P C, Neubig G. TRANX: A Transition-Based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation[OL]. arXiv Preprint, arXiv: 1810.02720.
[126]
Platanios E A, Pauls A, Roy S, et al. Value-Agnostic Conversational Semantic Parsing[C]// Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. 2021: 3666-3681.
[127]
Feng Z Y, Guo D Y, Tang D Y, et al. CodeBERT: A Pre-trained Model for Programming and Natural Languages[C]// Findings of the Association for Computational Linguistics:EMNLP 2020. 2020: 1536-1547.
[128]
Jiang X, Zheng Z R, Lyu C, et al. TreeBERT: A Tree-Based Pre-trained Model for Programming Language[C]// Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence. 2021: 54-63.
[129]
Min Q K, Shi Y F, Zhang Y. A Pilot Study for Chinese SQL Semantic Parsing[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. 2019: 3652-3658.
[130]
Campagna G, Semnani S, Kearns R, et al. A Few-Shot Semantic Parser for Wizard-of-Oz Dialogues with the Precise ThingTalk Representation[C]// Findings of the Association for Computational Linguistics:ACL 2022. 2022: 4021-4034.
[131]
Hui B Y, Geng R Y, Ren Q Y, et al. Dynamic Hybrid Relation Exploration Network for Cross-Domain Context-Dependent Semantic Parsing[C]// Proceedings of the 35th AAAI Conference on Artificial Intelligence. 2021: 13116-13124.
