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 976–1000 of 1202 papers

Title	Date	Tasks	Status
The Role of Output Vocabulary in T2T LMs for SPARQL Semantic Parsing	May 24, 2023	Graph Question AnsweringQuestion Answering	CodeCode Available
The Role of Semantic Parsing in Understanding Procedural Text	Feb 14, 2023	Semantic ParsingSemantic Role Labeling	CodeCode Available
Holistic Exploration on Universal Decompositional Semantic Parsing: Architecture, Data Augmentation, and LLM Paradigm	Jul 25, 2023	AttributeData Augmentation	CodeCode Available
Confidence Modeling for Neural Semantic Parsing	May 11, 2018	Semantic Parsing	CodeCode Available
Zero and Few-shot Semantic Parsing with Ambiguous Inputs	Jun 1, 2023	Semantic Parsing	CodeCode Available
Does Character-level Information Always Improve DRS-based Semantic Parsing?	Jun 4, 2023	Semantic Parsing	CodeCode Available
Neural Maximum Subgraph Parsing for Cross-Domain Semantic Dependency Analysis	Oct 1, 2018	Dependency ParsingSemantic Parsing	CodeCode Available
SParC: Cross-Domain Semantic Parsing in Context	Jun 5, 2019	DiversitySemantic Parsing	CodeCode Available
Context-Dependent Semantic Parsing over Temporally Structured Data	May 1, 2019	DecoderSemantic Parsing	CodeCode Available
Compositional Task-Oriented Parsing as Abstractive Question Answering	May 4, 2022	abstractive question answeringQuestion Answering	CodeCode Available
Generative Face Completion	Apr 19, 2017	Facial InpaintingSemantic Parsing	CodeCode Available
Function Assistant: A Tool for NL Querying of APIs	Jun 1, 2017	Natural Language QueriesSemantic Parsing	CodeCode Available
Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations	May 28, 2017	AMR ParsingSemantic Parsing	CodeCode Available
Scope-enhanced Compositional Semantic Parsing for DRT	Jul 2, 2024	Semantic ParsingSentence	CodeCode Available
Neural Semantic Parsing over Multiple Knowledge-bases	Feb 6, 2017	FormSemantic Parsing	CodeCode Available
Neural Semantic Parsing with Anonymization for Command Understanding in General-Purpose Service Robots	Jul 2, 2019	Semantic ParsingWord Embeddings	CodeCode Available
Neural Semantic Parsing with Extremely Rich Symbolic Meaning Representations	Apr 19, 2024	Semantic Parsing	CodeCode Available
Neural Semantic Parsing with Type Constraints for Semi-Structured Tables	Sep 1, 2017	DecoderEntity Linking	CodeCode Available
Compositionality as Lexical Symmetry	Jan 30, 2022	Data AugmentationInductive Bias	CodeCode Available
Fully-Semantic Parsing and Generation: the BabelNet Meaning Representation	May 1, 2022	Natural Language UnderstandingSemantic Parsing	CodeCode Available
Translate First Reorder Later: Leveraging Monotonicity in Semantic Parsing	Oct 10, 2022	Semantic Parsing	CodeCode Available
SPaR.txt, a cheap Shallow Parsing approach for Regulatory texts	Oct 4, 2021	Semantic Parsing	CodeCode Available
Imitation Learning of Agenda-based Semantic Parsers	Jan 1, 2015	Imitation LearningQuestion Answering	CodeCode Available
Compositional Generalization without Trees using Multiset Tagging and Latent Permutations	May 26, 2023	Inductive BiasSemantic Parsing	CodeCode Available
A Transition-Based Directed Acyclic Graph Parser for UCCA	Apr 3, 2017	Semantic ParsingUCCA Parsing	CodeCode Available

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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