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 826–850 of 1202 papers

Title	Date	Tasks	Status	Hype
Training an adaptive dialogue policy for interactive learning of visually grounded word meanings	Sep 29, 2017	Semantic Parsing	—Unverified	0
Towards Universal Semantic Tagging	Sep 29, 2017	Semantic Parsing	—Unverified	0
Object-oriented Neural Programming (OONP) for Document Understanding	Sep 26, 2017	document understandingObject	—Unverified	0
Abductive Matching in Question Answering	Sep 10, 2017	BIG-bench Machine LearningQuestion Answering	—Unverified	0
A Unified Framework for Structured Prediction: From Theory to Practice	Sep 1, 2017	AMR ParsingChunking	—Unverified	0
A Statistical Machine Translation Model with Forest-to-Tree Algorithm for Semantic Parsing	Sep 1, 2017	Machine TranslationSemantic Parsing	—Unverified	0
Cross-Lingual SRL Based upon Universal Dependencies	Sep 1, 2017	Semantic ParsingSemantic Role Labeling	—Unverified	0
Dealing with Co-reference in Neural Semantic Parsing	Sep 1, 2017	AMR ParsingSemantic Parsing	CodeCode Available	0
Maximum Margin Reward Networks for Learning from Explicit and Implicit Supervision	Sep 1, 2017	Dependency Parsingnamed-entity-recognition	—Unverified	0
Evaluating Hierarchies of Verb Argument Structure with Hierarchical Clustering	Sep 1, 2017	ClusteringLanguage Acquisition	—Unverified	0
Joint Concept Learning and Semantic Parsing from Natural Language Explanations	Sep 1, 2017	General ClassificationSemantic Parsing	—Unverified	0
Beyond Sentential Semantic Parsing: Tackling the Math SAT with a Cascade of Tree Transducers	Sep 1, 2017	coreference-resolutionCoreference Resolution	—Unverified	0
A Joint Sequential and Relational Model for Frame-Semantic Parsing	Sep 1, 2017	Knowledge DistillationMachine Translation	—Unverified	0
Neural Semantic Parsing with Type Constraints for Semi-Structured Tables	Sep 1, 2017	DecoderEntity Linking	CodeCode Available	0
QUINT: Interpretable Question Answering over Knowledge Bases	Sep 1, 2017	Named Entity Recognition (NER)Question Answering	—Unverified	0
Deep Neural Solver for Math Word Problems	Sep 1, 2017	Feature EngineeringMachine Translation	—Unverified	0
Recovering Question Answering Errors via Query Revision	Sep 1, 2017	Question AnsweringSemantic Parsing	—Unverified	0
SPARQL as a Foreign Language	Aug 25, 2017	Graph Question AnsweringKnowledge Base Question Answering	CodeCode Available	1
How Would You Say It? Eliciting Lexically Diverse Dialogue for Supervised Semantic Parsing	Aug 1, 2017	Semantic Parsing	—Unverified	0
Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks	Aug 1, 2017	RelationRelation Extraction	—Unverified	0
Towards Problem Solving Agents that Communicate and Learn	Aug 1, 2017	Semantic Parsing	—Unverified	0
Multilingual Semantic Parsing And Code-Switching	Aug 1, 2017	Domain AdaptationSemantic Parsing	CodeCode Available	0
Learning What is Essential in Questions	Aug 1, 2017	Information RetrievalQuestion Answering	CodeCode Available	0
Delexicalized transfer parsing for low-resource languages using transformed and combined treebanks	Aug 1, 2017	Cross-Lingual TransferDependency Parsing	—Unverified	0
SemEval-2017 Task 11: End-User Development using Natural Language	Aug 1, 2017	Semantic Parsing	—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