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
Confidence Modeling for Neural Semantic Parsing	May 11, 2018	Semantic Parsing	CodeCode Available
Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback	May 3, 2018	counterfactualSemantic Parsing	CodeCode Available
World Knowledge for Abstract Meaning Representation Parsing	May 1, 2018	Abstract Meaning RepresentationAMR Parsing	—Unverified
Integrating Generative Lexicon Event Structures into VerbNet	May 1, 2018	Semantic ParsingSemantic Role Labeling	—Unverified
Multitask Parsing Across Semantic Representations	May 1, 2018	Semantic ParsingUCCA Parsing	CodeCode Available
An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols	May 1, 2018	BIG-bench Machine LearningReading Comprehension	—Unverified
SPADE: Evaluation Dataset for Monolingual Phrase Alignment	May 1, 2018	Machine TranslationParaphrase Identification	—Unverified
Don't Annotate, but Validate: a Data-to-Text Method for Capturing Event Data	May 1, 2018	Semantic Parsing	—Unverified
Towards AMR-BR: A SemBank for Brazilian Portuguese Language	May 1, 2018	Entity LinkingMachine Reading Comprehension	—Unverified
Creation of a Balanced State-of-the-Art Multilayer Corpus for NLU	May 1, 2018	Abstractive Text SummarizationCoreference Resolution	CodeCode Available
Construction of Large-scale English Verbal Multiword Expression Annotated Corpus	May 1, 2018	Dependency ParsingSemantic Parsing	CodeCode Available
Huge Automatically Extracted Training-Sets for Multilingual Word SenseDisambiguation	May 1, 2018	Question AnsweringSemantic Parsing	—Unverified
EventWiki: A Knowledge Base of Major Events	May 1, 2018	Question AnsweringSemantic Parsing	—Unverified
A Parser for LTAG and Frame Semantics	May 1, 2018	Semantic Parsing	CodeCode Available
QUEST: A Natural Language Interface to Relational Databases	May 1, 2018	Relation ExtractionSemantic Parsing	—Unverified
Semantic Parsing with Syntax- and Table-Aware SQL Generation	Apr 23, 2018	Semantic Parsing	—Unverified
Cross-lingual Semantic Parsing	Apr 21, 2018	Semantic Parsing	—Unverified
Decoupling Structure and Lexicon for Zero-Shot Semantic Parsing	Apr 21, 2018	FormSemantic Parsing	CodeCode Available
Learning Joint Semantic Parsers from Disjoint Data	Apr 17, 2018	Dependency ParsingSemantic Dependency Parsing	CodeCode Available
Human Semantic Parsing for Person Re-identification	Mar 31, 2018	Person Re-IdentificationRepresentation Learning	—Unverified
Polyglot Semantic Parsing in APIs	Mar 19, 2018	Semantic ParsingTranslation	CodeCode Available
Semantic Parsing Natural Language into SPARQL: Improving Target Language Representation with Neural Attention	Mar 12, 2018	Semantic ParsingSentence	—Unverified
Cache Transition Systems for Graph Parsing	Mar 1, 2018	Dependency ParsingSemantic Parsing	—Unverified
AMUSE: Multilingual Semantic Parsing for Question Answering over Linked Data	Feb 26, 2018	FormKnowledge Base Question Answering	CodeCode Available
From Characters to Time Intervals: New Paradigms for Evaluation and Neural Parsing of Time Normalizations	Jan 1, 2018	Semantic CompositionSemantic 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