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 551–575 of 1202 papers

Title	Date	Tasks	Status
LIMIT-BERT : Linguistics Informed Multi-Task BERT	Nov 1, 2020	Language ModelingLanguage Modelling	CodeCode Available
Advancing Seq2seq with Joint Paraphrase Learning	Nov 1, 2020	Data AugmentationMulti-Task Learning	—Unverified
An Instance Level Approach for Shallow Semantic Parsing in Scientific Procedural Text	Nov 1, 2020	Relation PredictionSemantic Parsing	—Unverified
Uncertainty and Traffic-Aware Active Learning for Semantic Parsing	Nov 1, 2020	Active LearningSemantic Parsing	—Unverified
FLIN: A Flexible Natural Language Interface for Web Navigation	Oct 24, 2020	Semantic Parsingslot-filling	—Unverified
Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both?	Oct 24, 2020	Semantic Parsing	CodeCode Available
Overcoming Conflicting Data when Updating a Neural Semantic Parser	Oct 23, 2020	Semantic Parsing	CodeCode Available
Meta-Learning for Domain Generalization in Semantic Parsing	Oct 22, 2020	Domain GeneralizationMeta-Learning	—Unverified
Compositional Generalization via Semantic Tagging	Oct 22, 2020	Semantic ParsingTAG	—Unverified
Improving Company Valuations with Automated Knowledge Discovery, Extraction and Fusion	Oct 19, 2020	Semantic Parsing	—Unverified
Hierarchical Poset Decoding for Compositional Generalization in Language	Oct 15, 2020	DecoderQuestion Answering	—Unverified
Update Frequently, Update Fast: Retraining Semantic Parsing Systems in a Fraction of Time	Oct 15, 2020	Continual LearningGoal-Oriented Dialogue Systems	—Unverified
HUJI-KU at MRP~2020: Two Transition-based Neural Parsers	Oct 12, 2020	Semantic ParsingVocal Bursts Valence Prediction	—Unverified
Learning Adaptive Language Interfaces through Decomposition	Oct 11, 2020	Semantic Parsing	—Unverified
Compressing Transformer-Based Semantic Parsing Models using Compositional Code Embeddings	Oct 10, 2020	Semantic Parsing	—Unverified
Word Level Language Identification in English Telugu Code Mixed Data	Oct 9, 2020	Language IdentificationMachine Translation	—Unverified
Don't Parse, Insert: Multilingual Semantic Parsing with Insertion Based Decoding	Oct 8, 2020	Cross-Lingual TransferNatural Language Understanding	—Unverified
Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing	Oct 7, 2020	Domain AdaptationMeta-Learning	—Unverified
Adaptive Self-training for Few-shot Neural Sequence Labeling	Oct 7, 2020	Meta-Learningnamed-entity-recognition	—Unverified
A Tale of Two Linkings: Dynamically Gating between Schema Linking and Structural Linking for Text-to-SQL Parsing	Sep 30, 2020	Graph Neural NetworkSemantic Parsing	CodeCode Available
A Survey on Semantic Parsing from the perspective of Compositionality	Sep 29, 2020	Knowledge Base Question AnsweringQuestion Answering	—Unverified
Conversational Semantic Parsing	Sep 28, 2020	dialog state trackingSemantic Parsing	—Unverified
Fast semantic parsing with well-typedness guarantees	Sep 15, 2020	Dependency ParsingSemantic Parsing	—Unverified
MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark	Aug 21, 2020	BenchmarkingSemantic Parsing	—Unverified
Person image generation with semantic attention network for person re-identification	Aug 18, 2020	Image GenerationPerson Re-Identification	—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