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 1076–1100 of 1202 papers

Title	Date	Tasks	Status
On maximum spanning DAG algorithms for semantic DAG parsing	Jun 1, 2014	Dependency ParsingSemantic Parsing	—Unverified
Efficient Logical Inference for Semantic Processing	Jun 1, 2014	Natural Language InferenceSemantic Parsing	—Unverified
Low-Dimensional Embeddings of Logic	Jun 1, 2014	Reading ComprehensionSemantic Parsing	—Unverified
Proceedings of the ACL 2014 Workshop on Semantic Parsing	Jun 1, 2014	Semantic Parsing	—Unverified
Intermediary Semantic Representation through Proposition Structures	Jun 1, 2014	Dependency ParsingSemantic Parsing	—Unverified
From Treebank Parses to Episodic Logic and Commonsense Inference	Jun 1, 2014	Semantic Parsing	—Unverified
Software Requirements: A new Domain for Semantic Parsers	Jun 1, 2014	Semantic Parsing	—Unverified
Learning a Lexicon for Broad-coverage Semantic Parsing	Jun 1, 2014	Semantic Parsing	—Unverified
Semantic Parsing using Distributional Semantics and Probabilistic Logic	Jun 1, 2014	Natural Language InferenceQuestion Answering	—Unverified
Semantic Parsing for Text to 3D Scene Generation	Jun 1, 2014	Common Sense ReasoningScene Generation	—Unverified
Representing Caused Motion in Embodied Construction Grammar	Jun 1, 2014	Semantic Parsing	—Unverified
Modelling Events through Memory-based, Open-IE Patterns for Abstractive Summarization	Jun 1, 2014	Abstractive Text SummarizationParaphrase Generation	—Unverified
Knowledge-Based Question Answering as Machine Translation	Jun 1, 2014	Machine TranslationQuestion Answering	—Unverified
A Unified Model for Soft Linguistic Reordering Constraints in Statistical Machine Translation	Jun 1, 2014	Machine TranslationSemantic Parsing	—Unverified
A Step-wise Usage-based Method for Inducing Polysemy-aware Verb Classes	Jun 1, 2014	Semantic ParsingWord Sense Disambiguation	—Unverified
A Robust Approach to Aligning Heterogeneous Lexical Resources	Jun 1, 2014	Semantic ParsingSemantic Role Labeling	—Unverified
Training a Korean SRL System with Rich Morphological Features	Jun 1, 2014	Natural Language InferenceQuestion Answering	—Unverified
Semantic Parsing via Paraphrasing	Jun 1, 2014	Question AnsweringSemantic Parsing	—Unverified
Information Extraction over Structured Data: Question Answering with Freebase	Jun 1, 2014	Information RetrievalQuestion Answering	—Unverified
Response-based Learning for Grounded Machine Translation	Jun 1, 2014	Machine TranslationQuestion Answering	—Unverified
Context-dependent Semantic Parsing for Time Expressions	Jun 1, 2014	Semantic Parsing	—Unverified
A Discriminative Graph-Based Parser for the Abstract Meaning Representation	Jun 1, 2014	Abstract Meaning RepresentationAMR Parsing	—Unverified
Semantic Parsing for Single-Relation Question Answering	Jun 1, 2014	Open-Domain Question AnsweringQuestion Answering	—Unverified
Semantic Frame Identification with Distributed Word Representations	Jun 1, 2014	Semantic ParsingSemantic Role Labeling	—Unverified
Joint Syntactic and Semantic Parsing with Combinatory Categorial Grammar	Jun 1, 2014	Relation ExtractionSemantic Parsing	—Unverified

Show:10 25 50

← PrevPage 44 of 49Next →

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