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 126–150 of 1202 papers

Title	Date	Tasks	Status	Hype
The Natural Language Decathlon: Multitask Learning as Question Answering	Jun 20, 2018	DecoderDomain Adaptation	CodeCode Available	1
Code Generation from Natural Language with Less Prior Knowledge and More Monolingual Data	Aug 1, 2021	Code GenerationInductive Bias	CodeCode Available	1
Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing	Dec 23, 2020	DecoderDeep Attention	CodeCode Available	1
3D-to-2D Distillation for Indoor Scene Parsing	Apr 6, 2021	Scene ParsingSemantic Parsing	CodeCode Available	1
Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT	Nov 9, 2020	DRS ParsingLanguage Modeling	CodeCode Available	1
CogIE: An Information Extraction Toolkit for Bridging Texts and CogNet	Aug 1, 2021	Entity LinkingEntity Typing	CodeCode Available	1
COGS: A Compositional Generalization Challenge Based on Semantic Interpretation	Oct 12, 2020	PositionSemantic Parsing	CodeCode Available	1
AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data	Oct 9, 2020	AttributeNatural Questions	CodeCode Available	1
Complex Knowledge Base Question Answering: A Survey	Aug 15, 2021	Information RetrievalKnowledge Base Question Answering	CodeCode Available	1
Context Dependent Semantic Parsing: A Survey	Nov 2, 2020	Semantic ParsingSurvey	CodeCode Available	1
Contextual Semantic Parsing for Multilingual Task-Oriented Dialogues	Nov 4, 2021	Dialogue State TrackingMachine Translation	CodeCode Available	1
BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic Parsing	Jun 21, 2022	DecoderLanguage Modeling	CodeCode Available	1
Conversational Semantic Parsing for Dialog State Tracking	Oct 24, 2020	Decoderdialog state tracking	CodeCode Available	1
Benchmarking Meaning Representations in Neural Semantic Parsing	Nov 1, 2020	BenchmarkingSemantic Parsing	CodeCode Available	1
Code-Style In-Context Learning for Knowledge-Based Question Answering	Sep 9, 2023	Code GenerationIn-Context Learning	CodeCode Available	1
Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing	Mar 8, 2021	Human ParsingMulti-Person Pose Estimation	CodeCode Available	1
Constrained Language Models Yield Few-Shot Semantic Parsers	Apr 18, 2021	Semantic Parsing	CodeCode Available	1
Evaluating Scoped Meaning Representations	Feb 23, 2018	Natural Language UnderstandingNegation	CodeCode Available	1
Evaluating statistical language models as pragmatic reasoners	May 1, 2023	NegationSemantic Parsing	CodeCode Available	1
Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing	Jan 5, 2021	Dialogue State TrackingInductive Bias	CodeCode Available	1
Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases	Mar 6, 2019	Information RetrievalKnowledge Base Question Answering	CodeCode Available	1
Improving Compositional Generalization in Semantic Parsing	Oct 12, 2020	DecoderSemantic Parsing	CodeCode Available	1
Few-Shot Semantic Parsing for New Predicates	Jan 26, 2021	Few-Shot LearningMeta-Learning	CodeCode Available	1
Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold	Jun 29, 2017	Semantic Parsing	CodeCode Available	1
Neural Machine Translation for Query Construction and Composition	Jun 27, 2018	Code GenerationKnowledge Base Question Answering	CodeCode Available	1

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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