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 426–450 of 1202 papers

Title	Date	Tasks	Status
From Treebank Parses to Episodic Logic and Commonsense Inference	Jun 1, 2014	Semantic Parsing	—Unverified
Fully Automatic Semantic MT Evaluation	Jun 1, 2012	Machine TranslationSemantic Parsing	—Unverified
Constrained Semantic Forests for Improved Discriminative Semantic Parsing	Jul 1, 2015	Machine TranslationSemantic Parsing	—Unverified
Constructing Large Proposition Databases	May 1, 2012	Dependency ParsingMachine Translation	—Unverified
PMB5: Gaining More Insight into Neural Semantic Parsing with Challenging Benchmarks	Apr 12, 2024	Semantic ParsingText Generation	—Unverified
GCN-Sem at SemEval-2019 Task 1: Semantic Parsing using Graph Convolutional and Recurrent Neural Networks	Jun 1, 2019	Dependency ParsingSemantic Dependency Parsing	—Unverified
Generate-and-Retrieve: use your predictions to improve retrieval for semantic parsing	Sep 29, 2022	PredictionRetrieval	—Unverified
A Discriminative Graph-Based Parser for the Abstract Meaning Representation	Jun 1, 2014	Abstract Meaning RepresentationAMR Parsing	—Unverified
Generating Logical Forms from Graph Representations of Text and Entities	May 21, 2019	DecoderGraph Neural Network	—Unverified
Generating Syntactic Paraphrases	Oct 1, 2018	Data-to-Text GenerationMachine Translation	—Unverified
Generating Synthetic Data for Task-Oriented Semantic Parsing with Hierarchical Representations	Nov 3, 2020	DenoisingNatural Language Understanding	—Unverified
GRILLBot: An Assistant for Real-World Tasks with Neural Semantic Parsing and Graph-Based Representations	Aug 31, 2022	Semantic Parsing	—Unverified
Active Learning for Multilingual Semantic Parser	Jan 30, 2023	Active LearningSemantic Parsing	—Unverified
Did You Mean...? Confidence-based Trade-offs in Semantic Parsing	Mar 29, 2023	Semantic Parsing	—Unverified
German and French Neural Supertagging Experiments for LTAG Parsing	Jul 1, 2018	Dependency ParsingSemantic Parsing	—Unverified
GKR: the Graphical Knowledge Representation for semantic parsing	Jun 1, 2018	Natural Language InferenceSemantic Parsing	—Unverified
Global Methods for Cross-lingual Semantic Role and Predicate Labelling	Aug 1, 2014	Semantic 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
A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges	Jul 26, 2020	Information RetrievalQuestion Answering	—Unverified
Grammar-based Neural Text-to-SQL Generation	May 30, 2019	Semantic ParsingText to SQL	—Unverified
Grammar-Constrained Neural Semantic Parsing with LR Parsers	Aug 1, 2021	Semantic Parsing	—Unverified
DialSQL: Dialogue Based Structured Query Generation	Jul 1, 2018	Semantic ParsingTranslation	—Unverified
BME-UW at SRST-2019: Surface realization with Interpreted Regular Tree Grammars	Nov 1, 2019	DecoderSemantic Parsing	—Unverified
Graph Algebraic Combinatory Categorial Grammar	Jun 1, 2018	AMR ParsingMachine Translation	—Unverified
A Comparison of the Events and Relations Across ACE, ERE, TAC-KBP, and FrameNet Annotation Standards	Jun 1, 2014	Relation ExtractionSemantic Parsing	—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