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 376–400 of 1202 papers

Title	Date	Tasks	Status
Evaluation Strategies for Computational Construction Grammars	Dec 1, 2016	Semantic Parsing	—Unverified
Disambiguating Verbs by Collocation: Corpus Lexicography meets Natural Language Processing	May 1, 2014	Semantic ParsingWord Sense Disambiguation	—Unverified
EventWiki: A Knowledge Base of Major Events	May 1, 2018	Question AnsweringSemantic Parsing	—Unverified
Bottom-Up Unranked Tree-to-Graph Transducers for Translation into Semantic Graphs	Sep 1, 2019	ARCSemantic Parsing	—Unverified
Explaining Large Language Model-Based Neural Semantic Parsers (Student Abstract)	Jan 25, 2023	Language ModelingLanguage Modelling	—Unverified
Exploiting Frame-Semantics and Frame-Semantic Parsing for Automatic Extraction of Typological Information from Descriptive Grammars of Natural Languages	Sep 1, 2019	DescriptiveSemantic Parsing	—Unverified
A Semantic Parsing Algorithm to Solve Linear Ordering Problems	Feb 12, 2025	Multiple-choiceSemantic Parsing	—Unverified
Frame-Semantic Parsing	Mar 1, 2014	Semantic Parsing	—Unverified
Frame-Semantic Role Labeling with Heterogeneous Annotations	Jul 1, 2015	Domain AdaptationSemantic Parsing	—Unverified
Bootstrapping Multilingual Semantic Parsers using Large Language Models	Oct 13, 2022	Semantic ParsingTranslation	—Unverified
Dijkstra-WSA: A Graph-Based Approach to Word Sense Alignment	Jan 1, 2013	Semantic ParsingSemantic Role Labeling	—Unverified
A Bayesian Approach to Unsupervised Semantic Role Induction	Apr 1, 2012	Machine TranslationNatural Language Inference	—Unverified
A Hybrid Semantic Parsing Approach for Tabular Data Analysis	Oct 23, 2019	Semantic Parsing	—Unverified
Compositional Generalization for Natural Language Interfaces to Web APIs	Dec 9, 2021	Semantic Parsing	—Unverified
Extracting a bilingual semantic grammar from FrameNet-annotated corpora	May 1, 2014	Semantic ParsingText Generation	—Unverified
A Discriminative Graph-Based Parser for the Abstract Meaning Representation	Jun 1, 2014	Abstract Meaning RepresentationAMR Parsing	—Unverified
Extrinsic Evaluation of Machine Translation Metrics	Dec 20, 2022	Dialogue State TrackingMachine Translation	—Unverified
Fast and Accurate Capitalization and Punctuation for Automatic Speech Recognition Using Transformer and Chunk Merging	Aug 7, 2019	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Fast Forward Through Opportunistic Incremental Meaning Representation Construction	Jul 1, 2017	Decision MakingDialogue Understanding	—Unverified
FLIN: A Flexible Natural Language Interface for Web Navigation	Oct 24, 2020	Semantic Parsingslot-filling	—Unverified
Fast semantic parsing with well-typedness guarantees	Sep 15, 2020	Dependency ParsingSemantic Parsing	—Unverified
A Corpus of Preposition Supersenses	Aug 1, 2016	Machine TranslationSemantic Parsing	—Unverified
FedParsing: a Semi-Supervised Federated Learning Model on Semantic Parsing	Nov 16, 2021	Federated LearningSemantic Parsing	—Unverified
Did You Mean...? Confidence-based Trade-offs in Semantic Parsing	Mar 29, 2023	Semantic Parsing	—Unverified
Book Reviews: Ontology-Based Interpretation of Natural Language by Philipp Cimiano, Christina Unger and John McCrae	Jun 1, 2015	Semantic ParsingSemantic Role Labeling	—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