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
Bottom-Up Unranked Tree-to-Graph Transducers for Translation into Semantic Graphs	Sep 1, 2019	ARCSemantic Parsing	—Unverified
Improving Semantic Dependency Parsing with Syntactic Features	Sep 1, 2019	Dependency ParsingSemantic Dependency Parsing	—Unverified
Exploring Graph-Algebraic CCG Combinators for Syntactic-Semantic AMR Parsing	Sep 1, 2019	AMR ParsingSemantic Parsing	—Unverified
Parsing All: Syntax and Semantics, Dependencies and Spans	Aug 30, 2019	AllSemantic Parsing	CodeCode Available
Global Reasoning over Database Structures for Text-to-SQL Parsing	Aug 29, 2019	Graph Neural NetworkSemantic Parsing	CodeCode Available
A survey of cross-lingual features for zero-shot cross-lingual semantic parsing	Aug 27, 2019	Cross-Lingual Word EmbeddingsSemantic Parsing	—Unverified
Don't paraphrase, detect! Rapid and Effective Data Collection for Semantic Parsing	Aug 26, 2019	Semantic Parsing	CodeCode Available
Establishing Strong Baselines for the New Decade: Sequence Tagging, Syntactic and Semantic Parsing with BERT	Aug 14, 2019	Part-Of-Speech TaggingSemantic Parsing	CodeCode Available
Interpolated Convolutional Networks for 3D Point Cloud Understanding	Aug 13, 2019	3D Part Segmentation3D Point Cloud Classification	—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
Grammatical Sequence Prediction for Real-Time Neural Semantic Parsing	Jul 25, 2019	PredictionSemantic Parsing	—Unverified
Syntax-aware Neural Semantic Role Labeling	Jul 22, 2019	Semantic ParsingSemantic Role Labeling	CodeCode Available
Semantic Parsing with Dual Learning	Jul 10, 2019	Natural Language QueriesSemantic Parsing	CodeCode Available
Learning Neural Sequence-to-Sequence Models from Weak Feedback with Bipolar Ramp Loss	Jul 6, 2019	Machine TranslationSemantic Parsing	CodeCode Available
A Comparative Analysis of Knowledge-Intensive and Data-Intensive Semantic Parsers	Jul 4, 2019	Semantic Parsing	—Unverified
Neural Semantic Parsing with Anonymization for Command Understanding in General-Purpose Service Robots	Jul 2, 2019	Semantic ParsingWord Embeddings	CodeCode Available
Latent Structure Models for Natural Language Processing	Jul 1, 2019	Language ModelingLanguage Modelling	—Unverified
Discourse Representation Parsing for Sentences and Documents	Jul 1, 2019	Semantic ParsingSentence	—Unverified
AdaNSP: Uncertainty-driven Adaptive Decoding in Neural Semantic Parsing	Jul 1, 2019	DecoderSemantic Parsing	—Unverified
Reranking for Neural Semantic Parsing	Jul 1, 2019	Code GenerationReranking	—Unverified
Complex Question Decomposition for Semantic Parsing	Jul 1, 2019	Semantic Parsing	CodeCode Available
Semantic expressive capacity with bounded memory	Jun 27, 2019	Semantic Parsing	—Unverified
Compositional Semantic Parsing Across Graphbanks	Jun 27, 2019	Multi-Task LearningSemantic Parsing	—Unverified
Program Synthesis and Semantic Parsing with Learned Code Idioms	Jun 26, 2019	Code GenerationProgram Synthesis	CodeCode Available
Semantically Driven Auto-completion	Jun 22, 2019	Question AnsweringSemantic 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