SOTAVerified

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

Showing 101150 of 1202 papers

TitleStatusHype
Program Transfer for Answering Complex Questions over Knowledge BasesCode1
Progressive Semantic-Aware Style Transformation for Blind Face RestorationCode1
DPF: Learning Dense Prediction Fields with Weak SupervisionCode1
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL ParsersCode1
Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERTCode1
ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning ExamplesCode1
Rethinking Tabular Data Understanding with Large Language ModelsCode1
Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic ParsingCode1
Schema2QA: High-Quality and Low-Cost Q&A Agents for the Structured WebCode1
Semantic Parsing for Conversational Question Answering over Knowledge GraphsCode1
Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLsCode1
SPARQA: Skeleton-based Semantic Parsing for Complex Questions over Knowledge BasesCode1
AMR Parsing as Sequence-to-Graph TransductionCode1
Evaluating Scoped Meaning RepresentationsCode1
Evaluating statistical language models as pragmatic reasonersCode1
Structured Reordering for Modeling Latent Alignments in Sequence TransductionCode1
Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of the Question Answering Performance of the GPT LLM FamilyCode1
Syntax-augmented Multilingual BERT for Cross-lingual TransferCode1
Exploring Neural Methods for Parsing Discourse Representation StructuresCode1
Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic ScaffoldCode1
Generating Data for Symbolic Language with Large Language ModelsCode1
AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot FillingCode1
Text-to-Text Extraction and Verbalization of Biomedical Event GraphsCode1
FeTaQA: Free-form Table Question AnsweringCode1
Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic ParsingCode1
Focusing on Persons: Colorizing Old Images Learning from Modern Historical MoviesCode1
Break It Down: A Question Understanding BenchmarkCode1
Conversational Semantic Parsing for Dialog State TrackingCode1
CABINET: Content Relevance based Noise Reduction for Table Question AnsweringCode1
3D-to-2D Distillation for Indoor Scene ParsingCode1
Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene UnderstandingCode1
Calibrated Interpretation: Confidence Estimation in Semantic ParsingCode1
AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training DataCode1
Grammar Prompting for Domain-Specific Language Generation with Large Language ModelsCode1
Code Generation from Natural Language with Less Prior Knowledge and More Monolingual DataCode1
CogIE: An Information Extraction Toolkit for Bridging Texts and CogNetCode1
BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic ParsingCode1
Code-Style In-Context Learning for Knowledge-Based Question AnsweringCode1
Benchmarking Meaning Representations in Neural Semantic ParsingCode1
COGS: A Compositional Generalization Challenge Based on Semantic InterpretationCode1
Complex Knowledge Base Question Answering: A SurveyCode1
Human Pose Transfer by Adaptive Hierarchical DeformationCode1
Compositional Semantic Parsing on Semi-Structured TablesCode1
Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention NetworksCode1
Improved Semantic Role Labeling using Parameterized Neighborhood Memory AdaptationCode1
Bidirectional Attentive Memory Networks for Question Answering over Knowledge BasesCode1
Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-TrainingCode1
Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based TechniquesCode1
Constrained Language Models Yield Few-Shot Semantic ParsersCode1
Open-source Frame Semantic ParsingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ARTEMIS-DAAccuracy (Test)80.8Unverified
2SynTQA (Oracle)Test Accuracy77.5Unverified
3TabLaPAccuracy (Test)76.6Unverified
4SynTQA (GPT)Accuracy (Test)74.4Unverified
5Mix SCAccuracy (Test)73.6Unverified
6SynTQA (RF)Accuracy (Test)71.6Unverified
7CABINETAccuracy (Test)69.1Unverified
8NormTab+TabSQLifyAccuracy (Test)68.63Unverified
9Chain-of-TableAccuracy (Test)67.31Unverified
10Tab-PoTAccuracy (Test)66.78Unverified
#ModelMetricClaimedVerifiedStatus
1RESDSQL-3B + NatSQLAccuracy84.1Unverified
2code-davinci-002 175B (LEVER)Accuracy81.9Unverified
3RASAT+PICARDAccuracy75.5Unverified
4Graphix-3B + PICARDAccuracy74Unverified
5T5-3B + PICARDAccuracy71.9Unverified
6SADGA + GAPAccuracy70.1Unverified
7RATSQL + GAPAccuracy69.7Unverified
8RATSQL + Grammar-Augmented Pre-TrainingAccuracy69.6Unverified
9RATSQL + BERTAccuracy65.6Unverified
10Exact Set MatchingAccuracy19.7Unverified
#ModelMetricClaimedVerifiedStatus
1Dynamic Least-to-Most PromptingExact Match95Unverified
2LeARExact Match90.9Unverified
3T5-3B w/ Intermediate RepresentationsExact Match83.8Unverified
4Hierarchical Poset DecodingExact Match69Unverified
5Universal TransformerExact Match18.9Unverified
#ModelMetricClaimedVerifiedStatus
1ReaRevAccuracy76.4Unverified
2NSM+hAccuracy74.3Unverified
3CBR-KBQAAccuracy70Unverified
4STAGG (Yih et al., 2016)Accuracy63.9Unverified
5T5-11B (Raffel et al., 2020)Accuracy56.5Unverified
#ModelMetricClaimedVerifiedStatus
1CABINETDenotation accuracy (test)89.5Unverified
2TAPEX-Large (weak supervision)Denotation accuracy (test)89.5Unverified
3ReasTAP-Large (weak supervision)Denotation accuracy (test)89.2Unverified
4NL2SQL-BERTAccuracy89Unverified
5TAPAS-Large (weak supervision)Denotation accuracy (test)83.6Unverified
#ModelMetricClaimedVerifiedStatus
1PhraseTransformerAccuracy90.4Unverified
2TranxAccuracy86.2Unverified
3ASN (Rabinovich et al., 2017)Accuracy85.3Unverified
4ZH15 (Zhao and Huang, 2015)Accuracy84.2Unverified
#ModelMetricClaimedVerifiedStatus
1coarse2fineAccuracy88.2Unverified
2PhraseTransformerAccuracy87.9Unverified
3TranxAccuracy87.7Unverified
#ModelMetricClaimedVerifiedStatus
1PERIN + RobeCzechF192.36Unverified
2PERINF192.24Unverified
3HUJI-KUF158Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF180.52Unverified
2HUJI-KUF145Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF180.23Unverified
2HUJI-KUF152Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF194.16Unverified
2HUJI-KUF163Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF189.83Unverified
2HUJI-KUF162Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF192.73Unverified
2HUJI-KUF180Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF189.19Unverified
2HUJI-KUF154Unverified
#ModelMetricClaimedVerifiedStatus
1TAPEX-LargeDenotation Accuracy74.5Unverified
2TAPAS-LargeAccuracy67.2Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF176.4Unverified
2HUJI-KUF173Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF181.01Unverified
2HUJI-KUF175Unverified
#ModelMetricClaimedVerifiedStatus
1HSPEM66.18Unverified
#ModelMetricClaimedVerifiedStatus
1ReasonBERTRF1 Score41.3Unverified
#ModelMetricClaimedVerifiedStatus
1MeMCEExact40.3Unverified