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
Beyond Word Embeddings: Learning Entity and Concept Representations from Large Scale Knowledge Bases	Jan 1, 2018	Semantic ParsingWord Embeddings	—Unverified
Vietnamese Semantic Role Labelling	Nov 28, 2017	Semantic Parsing	CodeCode Available
Weakly-supervised Semantic Parsing with Abstract Examples	Nov 14, 2017	Semantic ParsingVisual Reasoning	CodeCode Available
The Challenge of Composition in Distributional and Formal Semantics	Nov 1, 2017	Natural Language InferenceSemantic Composition	—Unverified
Improving Black-box Speech Recognition using Semantic Parsing	Nov 1, 2017	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
SLING: A framework for frame semantic parsing	Oct 19, 2017	Semantic Frame ParsingSemantic Parsing	CodeCode Available
Towards Universal Semantic Tagging	Sep 29, 2017	Semantic Parsing	—Unverified
Training an adaptive dialogue policy for interactive learning of visually grounded word meanings	Sep 29, 2017	Semantic Parsing	—Unverified
Object-oriented Neural Programming (OONP) for Document Understanding	Sep 26, 2017	document understandingObject	—Unverified
Abductive Matching in Question Answering	Sep 10, 2017	BIG-bench Machine LearningQuestion Answering	—Unverified
A Unified Framework for Structured Prediction: From Theory to Practice	Sep 1, 2017	AMR ParsingChunking	—Unverified
A Joint Sequential and Relational Model for Frame-Semantic Parsing	Sep 1, 2017	Knowledge DistillationMachine Translation	—Unverified
QUINT: Interpretable Question Answering over Knowledge Bases	Sep 1, 2017	Named Entity Recognition (NER)Question Answering	—Unverified
Evaluating Hierarchies of Verb Argument Structure with Hierarchical Clustering	Sep 1, 2017	ClusteringLanguage Acquisition	—Unverified
Neural Semantic Parsing with Type Constraints for Semi-Structured Tables	Sep 1, 2017	DecoderEntity Linking	CodeCode Available
Recovering Question Answering Errors via Query Revision	Sep 1, 2017	Question AnsweringSemantic Parsing	—Unverified
Deep Neural Solver for Math Word Problems	Sep 1, 2017	Feature EngineeringMachine Translation	—Unverified
Dealing with Co-reference in Neural Semantic Parsing	Sep 1, 2017	AMR ParsingSemantic Parsing	CodeCode Available
Cross-Lingual SRL Based upon Universal Dependencies	Sep 1, 2017	Semantic ParsingSemantic Role Labeling	—Unverified
A Statistical Machine Translation Model with Forest-to-Tree Algorithm for Semantic Parsing	Sep 1, 2017	Machine TranslationSemantic Parsing	—Unverified
Maximum Margin Reward Networks for Learning from Explicit and Implicit Supervision	Sep 1, 2017	Dependency Parsingnamed-entity-recognition	—Unverified
Joint Concept Learning and Semantic Parsing from Natural Language Explanations	Sep 1, 2017	General ClassificationSemantic Parsing	—Unverified
Beyond Sentential Semantic Parsing: Tackling the Math SAT with a Cascade of Tree Transducers	Sep 1, 2017	coreference-resolutionCoreference Resolution	—Unverified
Embedded Semantic Lexicon Induction with Joint Global and Local Optimization	Aug 1, 2017	ClusteringQuestion Answering	—Unverified
Semantic Frames and Visual Scenes: Learning Semantic Role Inventories from Image and Video Descriptions	Aug 1, 2017	ClusteringSemantic 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