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 976–1000 of 1202 papers

Title	Date	Tasks	Status	Hype
Learning the Semantics of Manipulation Action	Dec 4, 2015	Semantic Parsing	—Unverified	0
Neural Enquirer: Learning to Query Tables with Natural Language	Dec 3, 2015	Semantic Parsing	—Unverified	0
Inducing Implicit Arguments from Comparable Texts: A Framework and Its Applications	Dec 1, 2015	Semantic ParsingSemantic Role Labeling	—Unverified	0
Semantic Parsing via l_0-norm-based Alignment	Sep 1, 2015	Semantic ParsingWord Alignment	—Unverified	0
A Supervised Semantic Parsing with Lexicon Extension and Syntactic Constraint	Sep 1, 2015	Semantic Parsing	—Unverified	0
Semantic Framework for Comparison Structures in Natural Language	Sep 1, 2015	Open-Domain Question AnsweringQuestion Answering	—Unverified	0
Broad-coverage CCG Semantic Parsing with AMR	Sep 1, 2015	AMR ParsingMachine Translation	—Unverified	0
Joint A* CCG Parsing and Semantic Role Labelling	Sep 1, 2015	Semantic ParsingSemantic Role Labeling	—Unverified	0
Any-language frame-semantic parsing	Sep 1, 2015	Cross-Lingual TransferQuestion Answering	—Unverified	0
Improving Semantic Parsing with Enriched Synchronous Context-Free Grammar	Sep 1, 2015	Machine TranslationSemantic Parsing	—Unverified	0
A Strong Lexical Matching Method for the Machine Comprehension Test	Sep 1, 2015	Common Sense ReasoningCoreference Resolution	—Unverified	0
Solving Geometry Problems: Combining Text and Diagram Interpretation	Sep 1, 2015	Automated Theorem ProvingMathematical Question Answering	CodeCode Available	0
Reversibility reconsidered: finite-state factors for efficient probabilistic sampling in parsing and generation	Sep 1, 2015	Machine TranslationSemantic Parsing	—Unverified	0
Automatically Solving Number Word Problems by Semantic Parsing and Reasoning	Sep 1, 2015	Semantic Parsing	—Unverified	0
ERSOM: A Structural Ontology Matching Approach Using Automatically Learned Entity Representation	Sep 1, 2015	Coreference ResolutionOntology Matching	—Unverified	0
Compositional Semantic Parsing on Semi-Structured Tables	Aug 3, 2015	Question AnsweringSemantic Parsing	CodeCode Available	1
Simple, Fast Semantic Parsing with a Tensor Kernel	Jul 2, 2015	Semantic Parsing	—Unverified	0
Round trips with meaning stopovers	Jul 1, 2015	Machine TranslationSemantic Parsing	—Unverified	0
Semantic Parsing for Textual Entailment	Jul 1, 2015	Dependency ParsingNatural Language Inference	—Unverified	0
Constrained Semantic Forests for Improved Discriminative Semantic Parsing	Jul 1, 2015	Machine TranslationSemantic Parsing	—Unverified	0
Scalable Large-Margin Structured Learning: Theory and Algorithms	Jul 1, 2015	Learning TheoryMachine Translation	—Unverified	0
Learning to Map Dependency Parses to Abstract Meaning Representations	Jul 1, 2015	Semantic Parsing	—Unverified	0
NiuParser: A Chinese Syntactic and Semantic Parsing Toolkit	Jul 1, 2015	Chinese Word SegmentationChunking	—Unverified	0
Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base	Jul 1, 2015	Entity LinkingGraph Generation	CodeCode Available	0
Matrix Factorization with Knowledge Graph Propagation for Unsupervised Spoken Language Understanding	Jul 1, 2015	Semantic ParsingSpoken Dialogue Systems	—Unverified	0

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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