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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 551–575 of 1202 papers

Title	Date	Tasks	Status
Lean Question Answering over Freebase from Scratch	Jun 1, 2015	ChunkingNamed Entity Recognition (NER)	—Unverified
Joint Relational Embeddings for Knowledge-based Question Answering	Oct 1, 2014	Named Entity Recognition (NER)Question Answering	—Unverified
Joint Multi-Decoder Framework with Hierarchical Pointer Network for Frame Semantic Parsing	Aug 1, 2021	DecoderSemantic Parsing	—Unverified
DBRouting: Routing End User Queries to Databases for Answerability	Jan 27, 2025	Logical ReasoningSemantic Parsing	—Unverified
Kicktionary-LOME: A Domain-Specific Multilingual Frame Semantic Parsing Model for Football Language	Aug 12, 2021	PredictionSemantic Parsing	—Unverified
kNN-ICL: Compositional Task-Oriented Parsing Generalization with Nearest Neighbor In-Context Learning	Dec 17, 2023	In-Context LearningPrompt Engineering	—Unverified
Knowledge-augmented Frame Semantic Parsing with Hybrid Prompt-tuning	Mar 25, 2023	Knowledge ProbingSemantic Parsing	—Unverified
Knowledge-Aware Conversational Semantic Parsing Over Web Tables	Sep 12, 2018	Entity TypingLanguage Modeling	—Unverified
Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing	May 1, 2020	DiversitySemantic Parsing	—Unverified
Knowledge Base Question Answering: A Semantic Parsing Perspective	Sep 12, 2022	AttributeKnowledge Base Question Answering	—Unverified
Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL	Jun 4, 2021	Semantic ParsingSQL Parsing	—Unverified
Knowledge-Driven Event Embedding for Stock Prediction	Dec 1, 2016	Information RetrievalOpen Information Extraction	—Unverified
Jointly Event Extraction and Visualization on Twitter via Probabilistic Modelling	Aug 1, 2016	Dimensionality ReductionEvent Extraction	—Unverified
Knowledge Informed Semantic Parsing for Conversational Question Answering	Aug 1, 2021	Conversational Question AnsweringDecoder	—Unverified
Joint learning of ontology and semantic parser from text	Jan 5, 2016	Semantic Parsing	—Unverified
Because Syntax Does Matter: Improving Predicate-Argument Structures Parsing with Syntactic Features	May 1, 2015	Dependency ParsingSemantic Dependency Parsing	—Unverified
L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models	Sep 29, 2023	Code GenerationMath	—Unverified
LAGr: Label Aligned Graphs for Better Systematic Generalization in Semantic Parsing	Nov 16, 2021	Semantic ParsingSystematic Generalization	—Unverified
Geo-Semantic-Parsing: AI-powered geoparsing by traversing semantic knowledge graphs	Mar 3, 2025	Knowledge GraphsSemantic Parsing	—Unverified
Joint Concept Learning and Semantic Parsing from Natural Language Explanations	Sep 1, 2017	General ClassificationSemantic Parsing	—Unverified
Joint Case Argument Identification for Japanese Predicate Argument Structure Analysis	Jul 1, 2015	Semantic ParsingSemantic Role Labeling	—Unverified
Language Generation via DAG Transduction	Jul 1, 2018	Semantic ParsingText Generation	—Unverified
Language-Guided World Models: A Model-Based Approach to AI Control	Jan 24, 2024	Semantic Parsing	—Unverified
Deep Learning on Graphs for Natural Language Processing	Jun 1, 2021	Deep Learninggraph construction	—Unverified
Data-Free Point Cloud Network for 3D Face Recognition	Nov 12, 2019	3D Object ClassificationFace Recognition	—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