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 876–900 of 1202 papers

Title	Date	Tasks	Status
Logical Parsing from Natural Language Based on a Neural Translation Model	May 9, 2017	Question AnsweringSemantic Parsing	—Unverified
Learning Structured Natural Language Representations for Semantic Parsing	Apr 27, 2017	Semantic Parsing	—Unverified
Abstract Syntax Networks for Code Generation and Semantic Parsing	Apr 25, 2017	Code GenerationDecoder	CodeCode Available
From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood	Apr 25, 2017	reinforcement-learningReinforcement Learning	CodeCode Available
Cross-domain Semantic Parsing via Paraphrasing	Apr 20, 2017	DiversityDomain Adaptation	CodeCode Available
Generative Face Completion	Apr 19, 2017	Facial InpaintingSemantic Parsing	CodeCode Available
The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing	Apr 7, 2017	Data AugmentationPOS	—Unverified
A Syntactic Neural Model for General-Purpose Code Generation	Apr 6, 2017	Code GenerationSemantic Parsing	CodeCode Available
A Transition-Based Directed Acyclic Graph Parser for UCCA	Apr 3, 2017	Semantic ParsingUCCA Parsing	CodeCode Available
MT/IE: Cross-lingual Open Information Extraction with Neural Sequence-to-Sequence Models	Apr 1, 2017	Machine TranslationOpen Information Extraction	—Unverified
Paraphrasing Revisited with Neural Machine Translation	Apr 1, 2017	Machine TranslationQuestion Answering	—Unverified
COVER: Covering the Semantically Tractable Questions	Apr 1, 2017	Automated Theorem ProvingSemantic Parsing	—Unverified
An Extensible Framework for Verification of Numerical Claims	Apr 1, 2017	Fact CheckingRumour Detection	—Unverified
Imitation learning for structured prediction in natural language processing	Apr 1, 2017	coreference-resolutionCoreference Resolution	—Unverified
Alto: Rapid Prototyping for Parsing and Translation	Apr 1, 2017	Machine TranslationSemantic Parsing	—Unverified
Integrated Learning of Dialog Strategies and Semantic Parsing	Apr 1, 2017	BIG-bench Machine LearningManagement	—Unverified
Integer Linear Programming formulations in Natural Language Processing	Apr 1, 2017	Dependency ParsingNatural Language Inference	—Unverified
The SUMMA Platform Prototype	Apr 1, 2017	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Universal Semantic Parsing	Feb 10, 2017	Question AnsweringSemantic Parsing	CodeCode Available
Neural Semantic Parsing over Multiple Knowledge-bases	Feb 6, 2017	FormSemantic Parsing	CodeCode Available
A constrained graph algebra for semantic parsing with AMRs	Jan 1, 2017	Semantic Parsing	—Unverified
Annotating and parsing to semantic frames: feedback from the French FrameNet project	Jan 1, 2017	Semantic Parsing	—Unverified
Automatically Tagging Constructions of Causation and Their Slot-Fillers	Jan 1, 2017	Semantic Parsing	—Unverified
Indexicals and Compositionality: Inside-Out or Outside-In?	Jan 1, 2017	Semantic Parsing	—Unverified
Project Notes on building a conversational parser on top of a text parser: Towards a causal language tagger for spoken Chinese	Jan 1, 2017	Semantic Parsing	—Unverified

Show:10 25 50

← PrevPage 36 of 49Next →

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