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 1026–1050 of 1202 papers

Title	Date	Tasks	Status
On the Proper Treatment of Quantifiers in Probabilistic Logic Semantics	Apr 1, 2015	Fine-Grained Opinion AnalysisNatural Language Inference	—Unverified
Semantic construction with graph grammars	Apr 1, 2015	Semantic Parsing	—Unverified
Rapid FrameNet annotation of spoken conversation transcripts	Apr 1, 2015	Semantic Parsing	—Unverified
Parsing Software Requirements with an Ontology-based Semantic Role Labeler	Apr 1, 2015	Semantic ParsingSemantic Role Labeling	—Unverified
Layers of Interpretation: On Grammar and Compositionality	Apr 1, 2015	Machine TranslationSemantic Parsing	—Unverified
Learning a Compositional Semantics for Freebase with an Open Predicate Vocabulary	Jan 1, 2015	Coreference ResolutionOpen Information Extraction	—Unverified
Domain Adaptation for Syntactic and Semantic Dependency Parsing Using Deep Belief Networks	Jan 1, 2015	Dependency ParsingDiversity	—Unverified
Semantic Parsing of Ambiguous Input through Paraphrasing and Verification	Jan 1, 2015	Language ModelingLanguage Modelling	—Unverified
Efficient Inference and Structured Learning for Semantic Role Labeling	Jan 1, 2015	Semantic ParsingSemantic Role Labeling	—Unverified
Imitation Learning of Agenda-based Semantic Parsers	Jan 1, 2015	Imitation LearningQuestion Answering	CodeCode Available
Supertagging: Introduction, learning, and application	Dec 19, 2014	Semantic ParsingTAG	—Unverified
Robust Semantics for Semantic Parsing	Dec 1, 2014	Reading ComprehensionSemantic Parsing	—Unverified
Type-Driven Incremental Semantic Parsing with Polymorphism	Nov 19, 2014	Semantic ParsingVocal Bursts Type Prediction	—Unverified
Comparing Representations of Semantic Roles for String-To-Tree Decoding	Oct 1, 2014	Language ModellingMachine Translation	—Unverified
Multi-Predicate Semantic Role Labeling	Oct 1, 2014	Machine TranslationSemantic Parsing	—Unverified
Joint Relational Embeddings for Knowledge-based Question Answering	Oct 1, 2014	Named Entity Recognition (NER)Question Answering	—Unverified
Semantic Kernels for Semantic Parsing	Oct 1, 2014	Semantic ParsingSemantic Textual Similarity	—Unverified
Morpho-syntactic Lexical Generalization for CCG Semantic Parsing	Oct 1, 2014	Question AnsweringSemantic Parsing	—Unverified
Semantic Parsing with Relaxed Hybrid Trees	Oct 1, 2014	Semantic Parsing	—Unverified
Werdy: Recognition and Disambiguation of Verbs and Verb Phrases with Syntactic and Semantic Pruning	Oct 1, 2014	Question AnsweringSemantic Parsing	—Unverified
Learning Compact Lexicons for CCG Semantic Parsing	Oct 1, 2014	Natural Language InferenceQuestion Answering	—Unverified
Semantic Parsing Using Content and Context: A Case Study from Requirements Elicitation	Oct 1, 2014	Semantic Parsing	—Unverified
Copenhagen-Malm\"o: Tree Approximations of Semantic Parsing Problems	Aug 1, 2014	Semantic ParsingSemantic Role Labeling	—Unverified
KUL-Eval: A Combinatory Categorial Grammar Approach for Improving Semantic Parsing of Robot Commands using Spatial Context	Aug 1, 2014	Semantic Parsing	—Unverified
Multilingual Semantic Parsing : Parsing Multiple Languages into Semantic Representations	Aug 1, 2014	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