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 1101–1125 of 1202 papers

Title	Date	Tasks	Status
Low-Dimensional Embeddings of Logic	Jun 1, 2014	Reading ComprehensionSemantic Parsing	—Unverified
Proceedings of the ACL 2014 Workshop on Semantic Parsing	Jun 1, 2014	Semantic Parsing	—Unverified
Using C5.0 and Exhaustive Search for Boosting Frame-Semantic Parsing Accuracy	May 1, 2014	Semantic ParsingSemantic Role Labeling	—Unverified
HuRIC: a Human Robot Interaction Corpus	May 1, 2014	Abstract Meaning RepresentationDomain Adaptation	—Unverified
The Multilingual Paraphrase Database	May 1, 2014	Document SummarizationInformation Retrieval	—Unverified
Not an Interlingua, But Close: Comparison of English AMRs to Chinese and Czech	May 1, 2014	Machine TranslationSemantic Parsing	—Unverified
NoSta-D Named Entity Annotation for German: Guidelines and Dataset	May 1, 2014	named-entity-recognitionNamed Entity Recognition	—Unverified
UnixMan Corpus: A Resource for Language Learning in the Unix Domain	May 1, 2014	Grounded language learningMachine Translation	—Unverified
The Procedure of Lexico-Semantic Annotation of Sk Treebank	May 1, 2014	Semantic ParsingSentence	—Unverified
Contextual Semantic Parsing using Crowdsourced Spatial Descriptions	May 1, 2014	Semantic ParsingSentence	—Unverified
Single Classifier Approach for Verb Sense Disambiguation based on Generalized Features	May 1, 2014	Semantic ParsingWord Sense Disambiguation	—Unverified
The Norwegian Dependency Treebank	May 1, 2014	Dependency ParsingMachine Translation	—Unverified
Disambiguating Verbs by Collocation: Corpus Lexicography meets Natural Language Processing	May 1, 2014	Semantic ParsingWord Sense Disambiguation	—Unverified
REFRACTIVE: An Open Source Tool to Extract Knowledge from Syntactic and Semantic Relations	May 1, 2014	Knowledge GraphsRelation Extraction	—Unverified
Predicate Matrix: extending SemLink through WordNet mappings	May 1, 2014	Natural Language InferenceQuestion Answering	—Unverified
Extracting a bilingual semantic grammar from FrameNet-annotated corpora	May 1, 2014	Semantic ParsingText Generation	—Unverified
First approach toward Semantic Role Labeling for Basque	May 1, 2014	feature selectionMachine Translation	—Unverified
Multilingual eXtended WordNet Knowledge Base: Semantic Parsing and Translation of Glosses	May 1, 2014	Machine TranslationSemantic Parsing	—Unverified
A Deep Architecture for Semantic Parsing	Apr 29, 2014	Semantic Parsing	—Unverified
Probabilistic Type Theory for Incremental Dialogue Processing	Apr 1, 2014	Dialogue ManagementSemantic Parsing	—Unverified
Inducing Example-based Semantic Frames from a Massive Amount of Verb Uses	Apr 1, 2014	Machine TranslationNatural Language Inference	—Unverified
Classifying Temporal Relations with Simple Features	Apr 1, 2014	Dependency ParsingRelation Classification	—Unverified
Empirically-motivated Generalizations of CCG Semantic Parsing Learning Algorithms	Apr 1, 2014	Question AnsweringSemantic Parsing	—Unverified
Frame-Semantic Parsing	Mar 1, 2014	Semantic Parsing	—Unverified
Integrative Semantic Dependency Parsing via Efficient Large-scale Feature Selection	Jan 23, 2014	Dependency Parsingfeature selection	—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