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 626–650 of 1202 papers

Title	Date	Tasks	Status
Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge	Jan 3, 2023	Semantic ParsingText to SQL	—Unverified
Towards Problem Solving Agents that Communicate and Learn	Aug 1, 2017	Semantic Parsing	—Unverified
Towards README-EVAL : Interpreting README File Instructions	Jun 1, 2014	Semantic Parsing	—Unverified
Towards Surface Realization with CCGs Induced from Dependencies	Jun 1, 2014	Semantic ParsingText Generation	—Unverified
Towards Theme Detection in Personal Finance Questions	Oct 4, 2021	Semantic ParsingSentence	—Unverified
Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction	Nov 16, 2021	Question AnsweringSemantic Parsing	—Unverified
Towards Universal Semantic Tagging	Sep 29, 2017	Semantic Parsing	—Unverified
Towards Zero-Shot Frame Semantic Parsing with Task Agnostic Ontologies and Simple Labels	May 5, 2023	Semantic ParsingSentence	—Unverified
Training a Korean SRL System with Rich Morphological Features	Jun 1, 2014	Natural Language InferenceQuestion Answering	—Unverified
Training an adaptive dialogue policy for interactive learning of visually grounded word meanings	Sep 29, 2017	Semantic Parsing	—Unverified
Training Table Question Answering via SQL Query Decomposition	Feb 19, 2024	Question AnsweringSemantic Parsing	—Unverified
Universal Decompositional Semantic Parsing	Oct 22, 2019	AttributePrediction	—Unverified
Transferable Natural Language Interface to Structured Queries aided by Adversarial Generation	Dec 4, 2018	Data AugmentationDomain Adaptation	—Unverified
Transfer Learning for Neural Semantic Parsing	Jun 14, 2017	Semantic ParsingTransfer Learning	—Unverified
Transferring Knowledge from Structure-aware Self-attention Language Model to Sequence-to-Sequence Semantic Parsing	Jan 16, 2022	Code GenerationKnowledge Distillation	—Unverified
Transferring Knowledge from Structure-aware Self-attention Language Model to Sequence-to-Sequence Semantic Parsing	Oct 1, 2022	Code GenerationKnowledge Distillation	—Unverified
Transformer Semantic Parsing	Dec 1, 2020	DecoderQuestion Answering	—Unverified
Transition-based Abstract Meaning Representation Parsing with Contextual Embeddings	Jun 13, 2022	Abstract Meaning RepresentationAMR Parsing	—Unverified
Transition-based DRS Parsing Using Stack-LSTMs	May 1, 2019	DRS ParsingSemantic Parsing	—Unverified
Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data	Sep 9, 2021	Data AugmentationSemantic Parsing	—Unverified
Translating a Math Word Problem to a Expression Tree	Oct 1, 2018	Machine TranslationMath	—Unverified
Translating Natural Language to SQL using Pointer-Generator Networks and How Decoding Order Matters	Nov 13, 2018	Question AnsweringSemantic Parsing	—Unverified
Translating Questions to SQL Queries with Generative Parsers Discriminatively Reranked	Dec 1, 2012	Semantic Parsing	—Unverified
Translational Symmetry-Aware Facade Parsing for 3D Building Reconstruction	Jun 2, 2021	Semantic ParsingSemantic Segmentation	—Unverified
TreePiece: Faster Semantic Parsing via Tree Tokenization	Mar 30, 2023	DecoderSemantic 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