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 851–875 of 1202 papers

Title	Date	Tasks	Status
XJSA at SemEval-2017 Task 4: A Deep System for Sentiment Classification in Twitter	Aug 1, 2017	General ClassificationSemantic Parsing	—Unverified
Learning What is Essential in Questions	Aug 1, 2017	Information RetrievalQuestion Answering	CodeCode Available
How Would You Say It? Eliciting Lexically Diverse Dialogue for Supervised Semantic Parsing	Aug 1, 2017	Semantic Parsing	—Unverified
Delexicalized transfer parsing for low-resource languages using transformed and combined treebanks	Aug 1, 2017	Cross-Lingual TransferDependency Parsing	—Unverified
Towards Problem Solving Agents that Communicate and Learn	Aug 1, 2017	Semantic Parsing	—Unverified
Multilingual Semantic Parsing And Code-Switching	Aug 1, 2017	Domain AdaptationSemantic Parsing	CodeCode Available
SemEval-2017 Task 11: End-User Development using Natural Language	Aug 1, 2017	Semantic Parsing	—Unverified
Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks	Aug 1, 2017	RelationRelation Extraction	—Unverified
Macro Grammars and Holistic Triggering for Efficient Semantic Parsing	Jul 25, 2017	Semantic ParsingSentence	CodeCode Available
3DCNN-DQN-RNN: A Deep Reinforcement Learning Framework for Semantic Parsing of Large-scale 3D Point Clouds	Jul 21, 2017	ClassificationDeep Reinforcement Learning	—Unverified
Evaluating Semantic Parsing against a Simple Web-based Question Answering Model	Jul 14, 2017	Question AnsweringSemantic Parsing	CodeCode Available
Domain Specific Automatic Question Generation from Text	Jul 1, 2017	Dependency ParsingQuestion Generation	—Unverified
Search-based Neural Structured Learning for Sequential Question Answering	Jul 1, 2017	Question AnsweringSemantic Parsing	—Unverified
Neural Architectures for Multilingual Semantic Parsing	Jul 1, 2017	DecoderMachine Translation	—Unverified
WebChild 2.0 : Fine-Grained Commonsense Knowledge Distillation	Jul 1, 2017	Knowledge DistillationSemantic Parsing	—Unverified
Generic Axiomatization of Families of Noncrossing Graphs in Dependency Parsing	Jul 1, 2017	Dependency ParsingSemantic Parsing	CodeCode Available
Semantic Parsing of Pre-university Math Problems	Jul 1, 2017	MathSemantic Parsing	—Unverified
NLP for Precision Medicine	Jul 1, 2017	Decision MakingEntity Linking	—Unverified
Fast Forward Through Opportunistic Incremental Meaning Representation Construction	Jul 1, 2017	Decision MakingDialogue Understanding	—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
Transfer Learning for Neural Semantic Parsing	Jun 14, 2017	Semantic ParsingTransfer Learning	—Unverified
Generic Axiomatization of Families of Noncrossing Graphs in Dependency Parsing	Jun 11, 2017	Dependency ParsingSemantic Parsing	CodeCode Available
Function Assistant: A Tool for NL Querying of APIs	Jun 1, 2017	Natural Language QueriesSemantic Parsing	CodeCode Available
Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations	May 28, 2017	AMR ParsingSemantic Parsing	CodeCode Available
Learning Semantic Correspondences in Technical Documentation	May 13, 2017	Semantic 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