Semantic Parsing

Semantic Parsing is the task of transducing natural language utterances into formal meaning representations. The target meaning representations can be defined according to a wide variety of formalisms. This include linguistically-motivated semantic representations that are designed to capture the meaning of any sentence such as λ-calculus or the abstract meaning representations. Alternatively, for more task-driven approaches to Semantic Parsing, it is common for meaning representations to represent executable programs such as SQL queries, robotic commands, smart phone instructions, and even general-purpose programming languages like Python and Java.

Source: Tranx: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation

Papers

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Showing 501–525 of 1202 papers

Title	Date	Tasks	Status
Improving Company Valuations with Automated Knowledge Discovery, Extraction and Fusion	Oct 19, 2020	Semantic Parsing	—Unverified
Biomedical Event Extraction Using Convolutional Neural Networks and Dependency Parsing	Jul 1, 2018	Dependency ParsingDiversity	—Unverified
Detecting novel metaphor using selectional preference information	Jun 1, 2016	Semantic ParsingSentiment Analysis	—Unverified
Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing	Nov 1, 2021	Out of Distribution (OOD) DetectionSemantic Parsing	—Unverified
Improving Generalization in Semantic Parsing by Increasing Natural Language Variation	Feb 13, 2024	Data AugmentationSemantic Parsing	—Unverified
Improving machine translation by training against an automatic semantic frame based evaluation metric	Aug 1, 2013	Machine TranslationSemantic Parsing	—Unverified
An in-depth look at Euclidean disk embeddings for structure preserving parsing	Nov 1, 2021	Dependency ParsingNatural Language Inference	—Unverified
Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning	Jul 3, 2024	In-Context LearningRetrieval	—Unverified
Improving Semantic Dependency Parsing with Syntactic Features	Sep 1, 2019	Dependency ParsingSemantic Dependency Parsing	—Unverified
Improving Semantic Parsing for Task Oriented Dialog	Feb 15, 2019	Language ModelingLanguage Modelling	—Unverified
A Deep Architecture for Semantic Parsing	Apr 29, 2014	Semantic Parsing	—Unverified
Detailed Garment Recovery from a Single-View Image	Aug 3, 2016	parameter estimationSemantic Parsing	—Unverified
Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak	Jul 30, 2020	ArticlesInformativeness	—Unverified
Dependency-based Hybrid Trees for Semantic Parsing	Sep 1, 2018	Semantic Parsing	—Unverified
Delexicalized transfer parsing for low-resource languages using transformed and combined treebanks	Aug 1, 2017	Cross-Lingual TransferDependency Parsing	—Unverified
Beyond Word Embeddings: Learning Entity and Concept Representations from Large Scale Knowledge Bases	Jan 1, 2018	Semantic ParsingWord Embeddings	—Unverified
An In-Context Schema Understanding Method for Knowledge Base Question Answering	Oct 22, 2023	In-Context LearningKnowledge Base Question Answering	—Unverified
Deep Semantic Parsing of Freehand Sketches with Homogeneous Transformation, Soft-Weighted Loss, and Staged Learning	Oct 14, 2019	Domain AdaptationImage Retrieval	—Unverified
Deep Portrait Delighting	Mar 22, 2022	Semantic ParsingSensitivity	—Unverified
Beyond Sentential Semantic Parsing: Tackling the Math SAT with a Cascade of Tree Transducers	Sep 1, 2017	coreference-resolutionCoreference Resolution	—Unverified
Deep Neural Solver for Math Word Problems	Sep 1, 2017	Feature EngineeringMachine Translation	—Unverified
Deep Neural Network applied to Part-of-Speech Tagging (R\'eseau de neurones profond pour l'\'etiquetage morpho-syntaxique) [in French]	Jul 1, 2014	Part-Of-Speech TaggingSemantic Parsing	—Unverified
Beyond Chains: Bridging Large Language Models and Knowledge Bases in Complex Question Answering	May 20, 2025	Knowledge Base Question AnsweringQuestion Answering	—Unverified
An Improved Coarse-to-Fine Method for Solving Generation Tasks	Apr 1, 2019	MathMath Word Problem Solving	—Unverified
Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation	May 18, 2022	Data AugmentationSemantic Parsing	—Unverified

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All datasets WikiTableQuestions spider CFQ WebQuestionsSP WikiSQL ATIS Geo PTG (czech, MRP 2020)AMR (chinese, MRP 2020)AMR (english, MRP 2020)DRG (english, MRP 2020)DRG (german, MRP 2020)

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ARTEMIS-DA	Accuracy (Test)	80.8	—	Unverified
2	SynTQA (Oracle)	Test Accuracy	77.5	—	Unverified
3	TabLaP	Accuracy (Test)	76.6	—	Unverified
4	SynTQA (GPT)	Accuracy (Test)	74.4	—	Unverified
5	Mix SC	Accuracy (Test)	73.6	—	Unverified
6	SynTQA (RF)	Accuracy (Test)	71.6	—	Unverified
7	CABINET	Accuracy (Test)	69.1	—	Unverified
8	NormTab+TabSQLify	Accuracy (Test)	68.63	—	Unverified
9	Chain-of-Table	Accuracy (Test)	67.31	—	Unverified
10	Tab-PoT	Accuracy (Test)	66.78	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RESDSQL-3B + NatSQL	Accuracy	84.1	—	Unverified
2	code-davinci-002 175B (LEVER)	Accuracy	81.9	—	Unverified
3	RASAT+PICARD	Accuracy	75.5	—	Unverified
4	Graphix-3B + PICARD	Accuracy	74	—	Unverified
5	T5-3B + PICARD	Accuracy	71.9	—	Unverified
6	SADGA + GAP	Accuracy	70.1	—	Unverified
7	RATSQL + GAP	Accuracy	69.7	—	Unverified
8	RATSQL + Grammar-Augmented Pre-Training	Accuracy	69.6	—	Unverified
9	RATSQL + BERT	Accuracy	65.6	—	Unverified
10	Exact Set Matching	Accuracy	19.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Dynamic Least-to-Most Prompting	Exact Match	95	—	Unverified
2	LeAR	Exact Match	90.9	—	Unverified
3	T5-3B w/ Intermediate Representations	Exact Match	83.8	—	Unverified
4	Hierarchical Poset Decoding	Exact Match	69	—	Unverified
5	Universal Transformer	Exact Match	18.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ReaRev	Accuracy	76.4	—	Unverified
2	NSM+h	Accuracy	74.3	—	Unverified
3	CBR-KBQA	Accuracy	70	—	Unverified
4	STAGG (Yih et al., 2016)	Accuracy	63.9	—	Unverified
5	T5-11B (Raffel et al., 2020)	Accuracy	56.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CABINET	Denotation accuracy (test)	89.5	—	Unverified
2	TAPEX-Large (weak supervision)	Denotation accuracy (test)	89.5	—	Unverified
3	ReasTAP-Large (weak supervision)	Denotation accuracy (test)	89.2	—	Unverified
4	NL2SQL-BERT	Accuracy	89	—	Unverified
5	TAPAS-Large (weak supervision)	Denotation accuracy (test)	83.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PhraseTransformer	Accuracy	90.4	—	Unverified
2	Tranx	Accuracy	86.2	—	Unverified
3	ASN (Rabinovich et al., 2017)	Accuracy	85.3	—	Unverified
4	ZH15 (Zhao and Huang, 2015)	Accuracy	84.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	coarse2fine	Accuracy	88.2	—	Unverified
2	PhraseTransformer	Accuracy	87.9	—	Unverified
3	Tranx	Accuracy	87.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PERIN + RobeCzech	F1	92.36	—	Unverified
2	PERIN	F1	92.24	—	Unverified
3	HUJI-KU	F1	58	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PERIN	F1	80.52	—	Unverified
2	HUJI-KU	F1	45	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PERIN	F1	80.23	—	Unverified
2	HUJI-KU	F1	52	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PERIN	F1	94.16	—	Unverified
2	HUJI-KU	F1	63	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PERIN	F1	89.83	—	Unverified
2	HUJI-KU	F1	62	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PERIN	F1	92.73	—	Unverified
2	HUJI-KU	F1	80	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PERIN	F1	89.19	—	Unverified
2	HUJI-KU	F1	54	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TAPEX-Large	Denotation Accuracy	74.5	—	Unverified
2	TAPAS-Large	Accuracy	67.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PERIN	F1	76.4	—	Unverified
2	HUJI-KU	F1	73	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PERIN	F1	81.01	—	Unverified
2	HUJI-KU	F1	75	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	HSP	EM	66.18	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ReasonBERTR	F1 Score	41.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MeMCE	Exact	40.3	—	Unverified