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 1001–1025 of 1202 papers

Title	Date	Tasks	Status
Language to Code: Learning Semantic Parsers for If-This-Then-That Recipes	Jul 1, 2015	Semantic Parsing	—Unverified
Environment-Driven Lexicon Induction for High-Level Instructions	Jul 1, 2015	Semantic ParsingVocal Bursts Intensity Prediction	CodeCode Available
Learning to Map Dependency Parses to Abstract Meaning Representations	Jul 1, 2015	Semantic Parsing	—Unverified
Matrix Factorization with Knowledge Graph Propagation for Unsupervised Spoken Language Understanding	Jul 1, 2015	Semantic ParsingSpoken Dialogue Systems	—Unverified
Graph parsing with s-graph grammars	Jul 1, 2015	Dependency ParsingSemantic Parsing	—Unverified
Semantic Parsing for Textual Entailment	Jul 1, 2015	Dependency ParsingNatural Language Inference	—Unverified
Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base	Jul 1, 2015	Entity LinkingGraph Generation	CodeCode Available
Event Detection and Domain Adaptation with Convolutional Neural Networks	Jul 1, 2015	Domain AdaptationEvent Detection	CodeCode Available
Joint Case Argument Identification for Japanese Predicate Argument Structure Analysis	Jul 1, 2015	Semantic ParsingSemantic Role Labeling	—Unverified
Unsupervised Semantic Parsing of Video Collections	Jun 28, 2015	Semantic ParsingUnsupervised semantic parsing	—Unverified
Riga: from FrameNet to Semantic Frames with C6.0 Rules	Jun 1, 2015	Dependency ParsingSemantic Parsing	—Unverified
Turku: Semantic Dependency Parsing as a Sequence Classification	Jun 1, 2015	ClassificationDependency Parsing	—Unverified
Event Extraction as Frame-Semantic Parsing	Jun 1, 2015	Event ExtractionSemantic Parsing	—Unverified
Identifying Various Kinds of Event Mentions in K-Parser Output	Jun 1, 2015	Machine TranslationQuestion Answering	—Unverified
Lean Question Answering over Freebase from Scratch	Jun 1, 2015	ChunkingNamed Entity Recognition (NER)	—Unverified
Interpreting Questions with a Log-Linear Ranking Model in a Virtual Patient Dialogue System	Jun 1, 2015	Question AnsweringSemantic Parsing	—Unverified
Book Reviews: Ontology-Based Interpretation of Natural Language by Philipp Cimiano, Christina Unger and John McCrae	Jun 1, 2015	Semantic ParsingSemantic Role Labeling	—Unverified
Deep Learning and Continuous Representations for Natural Language Processing	May 1, 2015	Deep LearningInformation Retrieval	—Unverified
Response-based Learning for Machine Translation of Open-domain Database Queries	May 1, 2015	Machine TranslationModel Selection	—Unverified
Using Positional Suffix Trees to Perform Agile Tree Kernel Calculation	May 1, 2015	Named Entity Recognition (NER)Relation Extraction	—Unverified
The Logic of AMR: Practical, Unified, Graph-Based Sentence Semantics for NLP	May 1, 2015	Abstract Meaning RepresentationMachine Translation	—Unverified
Open-Domain Semantic Parsing with Boxer	May 1, 2015	Semantic ParsingWord Sense Disambiguation	—Unverified
Because Syntax Does Matter: Improving Predicate-Argument Structures Parsing with Syntactic Features	May 1, 2015	Dependency ParsingSemantic Dependency Parsing	—Unverified
Semantic parsing of speech using grammars learned with weak supervision	May 1, 2015	Language ModellingSemantic Parsing	—Unverified
Grounded Semantic Parsing for Complex Knowledge Extraction	May 1, 2015	ClusteringEvent Extraction	—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