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 826–850 of 1202 papers

Title	Date	Tasks	Status
A Data Efficient End-To-End Spoken Language Understanding Architecture	Feb 14, 2020	ChunkingLanguage Modeling	—Unverified
Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation	Nov 16, 2021	Data AugmentationSemantic Parsing	—Unverified
Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation	May 18, 2022	Data AugmentationSemantic Parsing	—Unverified
A Deep Architecture for Semantic Parsing	Apr 29, 2014	Semantic Parsing	—Unverified
A Discriminative Graph-Based Parser for the Abstract Meaning Representation	Jun 1, 2014	Abstract Meaning RepresentationAMR Parsing	—Unverified
A Double-Graph Based Framework for Frame Semantic Parsing	Oct 16, 2021	graph constructionSemantic Parsing	—Unverified
ARTEMIS-DA: An Advanced Reasoning and Transformation Engine for Multi-Step Insight Synthesis in Data Analytics	Dec 18, 2024	Code GenerationInformation Retrieval	—Unverified
Advancing Seq2seq with Joint Paraphrase Learning	Nov 1, 2020	Data AugmentationMulti-Task Learning	—Unverified
A Full End-to-End Semantic Role Labeler, Syntactic-agnostic Over Syntactic-aware?	Aug 1, 2018	DecoderMachine Translation	—Unverified
A Globally Normalized Neural Model for Semantic Parsing	Jun 7, 2021	modelSemantic Parsing	—Unverified
A Joint Sequential and Relational Model for Frame-Semantic Parsing	Sep 1, 2017	Knowledge DistillationMachine Translation	—Unverified
A Knowledge-Guided Framework for Frame Identification	Aug 1, 2021	Semantic ParsingSentence	—Unverified
ALB at SemEval-2018 Task 10: A System for Capturing Discriminative Attributes	Jun 1, 2018	AttributeDependency Parsing	—Unverified
Aligning Formal Meaning Representations with Surface Strings for Wide-Coverage Text Generation	Aug 1, 2013	Semantic ParsingText Generation	—Unverified
Alpage: Transition-based Semantic Graph Parsing with Syntactic Features	Aug 1, 2014	Dependency ParsingSemantic Parsing	—Unverified
Alto: Rapid Prototyping for Parsing and Translation	Apr 1, 2017	Machine TranslationSemantic Parsing	—Unverified
A Methodology for Creating Question Answering Corpora Using Inverse Data Annotation	Apr 16, 2020	Question AnsweringSemantic Parsing	—Unverified
A Multi-layer LSTM-based Approach for Robot Command Interaction Modeling	Nov 13, 2018	Natural Language UnderstandingSemantic Parsing	—Unverified
A Multimodal LDA Model integrating Textual, Cognitive and Visual Modalities	Oct 1, 2013	Semantic Parsing	—Unverified
Analyse s\'emantique robuste par apprentissage antagoniste pour la g\'en\'eralisation de domaine (Robust Semantic Parsing with Adversarial Learning for Domain Generalization )	Jun 1, 2020	Domain GeneralizationSemantic Parsing	—Unverified
An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols	May 1, 2018	BIG-bench Machine LearningReading Comprehension	—Unverified
An Annotation Scheme for Quantifier Scope Disambiguation	May 1, 2012	Semantic ParsingSentence	—Unverified
An Automatic Method for Building a Data-to-Text Generator	Aug 1, 2013	Semantic ParsingText Generation	—Unverified
An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge	Jul 1, 2017	Information RetrievalQuestion Answering	—Unverified
A Neural Approach to KGQA via SPARQL Silhouette Generation	Nov 16, 2021	Graph Question AnsweringMachine Translation	—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