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 651–675 of 1202 papers

Title	Date	Tasks	Status
T\"upa at SemEval-2019 Task1: (Almost) feature-free Semantic Parsing	Jun 1, 2019	AllData Augmentation	—Unverified
Turku: Broad-Coverage Semantic Parsing with Rich Features	Aug 1, 2014	Dependency ParsingSemantic Parsing	—Unverified
Turku: Semantic Dependency Parsing as a Sequence Classification	Jun 1, 2015	ClassificationDependency Parsing	—Unverified
Type-Driven Incremental Semantic Parsing with Polymorphism	Nov 19, 2014	Semantic ParsingVocal Bursts Type Prediction	—Unverified
UC Davis at SemEval-2019 Task 1: DAG Semantic Parsing with Attention-based Decoder	Jun 1, 2019	DecoderFeature Engineering	—Unverified
UCL+Sheffield at SemEval-2016 Task 8: Imitation learning for AMR parsing with an alpha-bound	Jun 1, 2016	AMR ParsingImitation Learning	—Unverified
\'UFAL-Oslo at MRP 2019: Garage Sale Semantic Parsing	Nov 1, 2019	Semantic Parsing	—Unverified
Un algorithme d’analyse sémantique fondée sur les graphes via le problème de l’arborescence généralisée couvrante (A graph-based semantic parsing algorithm via the generalized spanning arborescence problem)	Jun 1, 2022	Semantic Parsing	—Unverified
Unanimous Prediction for 100\% Precision with Application to Learning Semantic Mappings	Aug 1, 2016	Question AnsweringSemantic Parsing	—Unverified
Uncertainty and Traffic-Aware Active Learning for Semantic Parsing	Nov 1, 2020	Active LearningSemantic Parsing	—Unverified
Unfreeze with Care: Space-Efficient Fine-Tuning of Semantic Parsing Models	Mar 5, 2022	Language ModelingLanguage Modelling	—Unverified
Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database	Nov 9, 2022	Question AnsweringSemantic Parsing	—Unverified
Universal Discourse Representation Structure Parsing	Jun 1, 2021	Semantic Parsing	—Unverified
Universal Semantic Annotator: the First Unified API for WSD, SRL and Semantic Parsing	Jun 1, 2022	Semantic ParsingSemantic Role Labeling	—Unverified
UnixMan Corpus: A Resource for Language Learning in the Unix Domain	May 1, 2014	Grounded language learningMachine Translation	—Unverified
Unsupervised frame based Semantic Role Induction: application to French and English	Jul 1, 2012	Semantic ParsingSemantic Role Labeling	—Unverified
Unsupervised Induction of Frame-Semantic Representations	Jun 1, 2012	Semantic ParsingSemantic Role Labeling	—Unverified
Unsupervised Semantic Parsing of Video Collections	Jun 28, 2015	Semantic ParsingUnsupervised semantic parsing	—Unverified
Update Frequently, Update Fast: Retraining Semantic Parsing Systems in a Fraction of Time	May 16, 2021	Semantic Parsing	—Unverified
Using C5.0 and Exhaustive Search for Boosting Frame-Semantic Parsing Accuracy	May 1, 2014	Semantic ParsingSemantic Role Labeling	—Unverified
Using language technology resources and tools to construct Swedish FrameNet	Aug 1, 2014	Semantic Parsing	—Unverified
Using Positional Suffix Trees to Perform Agile Tree Kernel Calculation	May 1, 2015	Named Entity Recognition (NER)Relation Extraction	—Unverified
Using Semantic Unification to Generate Regular Expressions from Natural Language	Jun 1, 2013	Semantic Parsing	—Unverified
Using Shallow Semantic Parsing and Relation Extraction for Finding Contradiction in Text	Oct 1, 2013	Document SummarizationMulti-Document Summarization	—Unverified
Usnea: An Authorship Tool for Interactive Fiction using Retrieval Based Semantic Parsing	Jul 1, 2020	RetrievalSemantic 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