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 801–825 of 1202 papers

Title	Date	Tasks	Status
Learning Better Structured Representations Using Low-rank Adaptive Label Smoothing	Jan 1, 2021	Generalization BoundsMachine Translation	—Unverified
Learning Compact Lexicons for CCG Semantic Parsing	Oct 1, 2014	Natural Language InferenceQuestion Answering	—Unverified
Learning Compositional Semantics for Open Domain Semantic Parsing	Dec 1, 2012	Machine TranslationSemantic Composition	—Unverified
Learning Cross-lingual Distributed Logical Representations for Semantic Parsing	Jun 14, 2018	Semantic Parsing	—Unverified
Learning Executable Semantic Parsers for Natural Language Understanding	Mar 22, 2016	Natural Language UnderstandingQuestion Answering	—Unverified
Learning Programmatic Idioms for Scalable Semantic Parsing	Apr 19, 2019	Code GenerationSemantic Parsing	—Unverified
Learning Semantic Correspondences in Technical Documentation	May 13, 2017	Semantic Parsing	—Unverified
Learning Structured Natural Language Representations for Semantic Parsing	Apr 27, 2017	Semantic Parsing	—Unverified
Learning the Semantics of Manipulation Action	Dec 4, 2015	Semantic Parsing	—Unverified
Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts	Oct 30, 2022	Language ModelingLanguage Modelling	—Unverified
Learning to Interpret Natural Language Instructions	Jun 1, 2012	Language ModellingSemantic Parsing	—Unverified
Learning to Jointly Predict Ellipsis and Comparison Structures	Aug 1, 2016	Question AnsweringReading Comprehension	—Unverified
Learning to Make Inferences in a Semantic Parsing Task	Jan 1, 2016	Machine TranslationNatural Language Inference	—Unverified
Learning to Map Dependency Parses to Abstract Meaning Representations	Jul 1, 2015	Semantic Parsing	—Unverified
Learning to Retrieve Iteratively for In-Context Learning	Jun 20, 2024	Combinatorial OptimizationIn-Context Learning	—Unverified
Learning to Transpile AMR into SPARQL	Dec 15, 2021	Abstract Meaning RepresentationKnowledge Base Question Answering	—Unverified
Learning to Transpile AMR into SPARQL	Jan 16, 2022	Abstract Meaning RepresentationKnowledge Base Question Answering	—Unverified
Learning Web-based Procedures by Reasoning over Explanations and Demonstrations in Context	Jul 1, 2020	Language AcquisitionProgram Synthesis	—Unverified
Lemmatising Serbian as Category Tagging with Bidirectional Sequence Classification	May 1, 2012	ClassificationGeneral Classification	—Unverified
Leveraging Data Recasting to Enhance Tabular Reasoning	Nov 23, 2022	Semantic Parsing	—Unverified
Leveraging Domain-Independent Information in Semantic Parsing	Aug 1, 2013	Semantic Parsing	—Unverified
Lexicon-injected Semantic Parsing for Task-Oriented Dialog	Nov 26, 2022	Semantic Parsing	—Unverified
LIA: A Natural Language Programmable Personal Assistant	Nov 1, 2018	Semantic Parsing	—Unverified
Light Textual Inference for Semantic Parsing	Dec 1, 2012	Natural Language InferenceSemantic Parsing	—Unverified
LIMIT-BERT : Linguistic Informed Multi-Task BERT	Oct 31, 2019	Multi-Task LearningPOS	—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