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 576–600 of 1202 papers

Title	Date	Tasks	Status
Ar-Spider: Text-to-SQL in Arabic	Feb 22, 2024	Semantic ParsingText to SQL	—Unverified
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing	May 24, 2022	DecoderIn-Context Learning	—Unverified
Comparing Knowledge-Intensive and Data-Intensive Models for English Resource Semantic Parsing	Mar 1, 2021	Semantic Parsing	—Unverified
Combining Improvements for Exploiting Dependency Trees in Neural Semantic Parsing	Dec 25, 2021	Ensemble LearningSemantic Parsing	—Unverified
A Robust Approach to Aligning Heterogeneous Lexical Resources	Jun 1, 2014	Semantic ParsingSemantic Role Labeling	—Unverified
Evaluating Persian Tokenizers	Feb 22, 2022	Language ModelingLanguage Modelling	—Unverified
Combining Formal and Distributional Models of Temporal and Intensional Semantics	Jun 1, 2014	Reading ComprehensionSemantic Parsing	—Unverified
Argument structure of adverbial derivatives in Russian	Aug 1, 2014	Machine TranslationNatural Language Inference	—Unverified
A Corpus and Semantic Parser for Multilingual Natural Language Querying of OpenStreetMap	Jun 1, 2016	Machine TranslationSemantic Parsing	—Unverified
Evaluating Hierarchies of Verb Argument Structure with Hierarchical Clustering	Sep 1, 2017	ClusteringLanguage Acquisition	—Unverified
Evaluating Byte and Wordpiece Level Models for Massively Multilingual Semantic Parsing	Dec 14, 2022	Cross-Lingual TransferSemantic Parsing	—Unverified
EUSP: An Easy-to-Use Semantic Parsing PlatForm	Nov 1, 2019	Semantic Parsing	—Unverified
ERSOM: A Structural Ontology Matching Approach Using Automatically Learned Entity Representation	Sep 1, 2015	Coreference ResolutionOntology Matching	—Unverified
A Probabilistic-Logic based Commonsense Representation Framework for Modelling Inferences with Multiple Antecedents and Varying Likelihoods	Nov 30, 2022	Knowledge GraphsQuestion Answering	—Unverified
Error-Aware Interactive Semantic Parsing of OpenStreetMap	Jun 22, 2021	Semantic Parsing	—Unverified
Equation Parsing : Mapping Sentences to Grounded Equations	Nov 1, 2016	Semantic Parsing	—Unverified
CMU: Arc-Factored, Discriminative Semantic Dependency Parsing	Aug 1, 2014	ARCDependency Parsing	—Unverified
Enhancing The RATP-DECODA Corpus With Linguistic Annotations For Performing A Large Range Of NLP Tasks	May 1, 2016	Semantic Parsing	—Unverified
Classifying Temporal Relations with Simple Features	Apr 1, 2014	Dependency ParsingRelation Classification	—Unverified
Enhancing Text-to-SQL Capabilities of Large Language Models via Domain Database Knowledge Injection	Sep 24, 2024	HallucinationSemantic Parsing	—Unverified
Self-Enhancing Multi-filter Sequence-to-Sequence Model	Sep 25, 2021	ClusteringDecoder	—Unverified
Enhancing Key-Value Memory Neural Networks for Knowledge Based Question Answering	Jun 1, 2019	Question AnsweringReading Comprehension	—Unverified
CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsing	Oct 13, 2022	Data AugmentationSemantic Parsing	—Unverified
A Pointer Network Architecture for Context-Dependent Semantic Parsing	Apr 1, 2019	Semantic Parsing	—Unverified
A Globally Normalized Neural Model for Semantic Parsing	Jun 7, 2021	modelSemantic 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