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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 676–700 of 1202 papers

Title	Date	Tasks	Status	Hype
Generating Logical Forms from Graph Representations of Text and Entities	May 21, 2019	DecoderGraph Neural Network	—Unverified	0
AMR Parsing as Sequence-to-Graph Transduction	May 21, 2019	AMR ParsingSemantic Parsing	CodeCode Available	1
Questions in Dependent Type Semantics	May 1, 2019	Natural Language InferenceRTE	—Unverified	0
Proceedings of the IWCS Shared Task on Semantic Parsing	May 1, 2019	Semantic Parsing	—Unverified	0
Linguistic Information in Neural Semantic Parsing with Multiple Encoders	May 1, 2019	DRS ParsingMachine Translation	—Unverified	0
Transition-based DRS Parsing Using Stack-LSTMs	May 1, 2019	DRS ParsingSemantic Parsing	—Unverified	0
Context-Dependent Semantic Parsing over Temporally Structured Data	May 1, 2019	DecoderSemantic Parsing	CodeCode Available	0
Multi-Task Learning for Semantic Parsing with Cross-Domain Sketch	May 1, 2019	DecoderMulti-Task Learning	—Unverified	0
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision	Apr 26, 2019	Image-text RetrievalObject	CodeCode Available	0
Good-Enough Compositional Data Augmentation	Apr 21, 2019	Data AugmentationDiagnostic	CodeCode Available	0
Learning Programmatic Idioms for Scalable Semantic Parsing	Apr 19, 2019	Code GenerationSemantic Parsing	—Unverified	0
CraftAssist Instruction Parsing: Semantic Parsing for a Minecraft Assistant	Apr 17, 2019	MinecraftSemantic Parsing	—Unverified	0
Unsupervised Person Image Generation with Semantic Parsing Transformation	Apr 6, 2019	Image GenerationImage Manipulation	CodeCode Available	0
An Improved Coarse-to-Fine Method for Solving Generation Tasks	Apr 1, 2019	MathMath Word Problem Solving	—Unverified	0
A Pointer Network Architecture for Context-Dependent Semantic Parsing	Apr 1, 2019	Semantic Parsing	—Unverified	0
Domain Adaptation for Low-Resource Neural Semantic Parsing	Apr 1, 2019	Domain AdaptationSemantic Parsing	—Unverified	0
Neural Program Planner for Structured Predictions	Mar 23, 2019	Machine TranslationProgram Synthesis	—Unverified	0
A Type-coherent, Expressive Representation as an Initial Step to Language Understanding	Mar 22, 2019	Semantic Parsing	—Unverified	0
Content Differences in Syntactic and Semantic Representations	Mar 15, 2019	Semantic ParsingUCCA Parsing	CodeCode Available	0
HLT@SUDA at SemEval 2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing	Mar 11, 2019	General ClassificationSemantic Parsing	—Unverified	0
Practical Semantic Parsing for Spoken Language Understanding	Mar 11, 2019	Multi-Task LearningQuestion Answering	—Unverified	0
Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases	Mar 6, 2019	Information RetrievalKnowledge Base Question Answering	CodeCode Available	1
SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA	Mar 6, 2019	Semantic ParsingUCCA Parsing	—Unverified	0
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs	Mar 1, 2019	Question AnsweringReading Comprehension	CodeCode Available	0
Learning to Generalize from Sparse and Underspecified Rewards	Feb 19, 2019	Bayesian OptimizationSemantic Parsing	CodeCode Available	0

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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