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 126–150 of 1202 papers

TitleStatusHype
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)80.8—Unverified
2SynTQA (Oracle)Test Accuracy77.5—Unverified
3TabLaPAccuracy (Test)76.6—Unverified
4SynTQA (GPT)Accuracy (Test)74.4—Unverified
5Mix SCAccuracy (Test)73.6—Unverified
6SynTQA (RF)Accuracy (Test)71.6—Unverified
7CABINETAccuracy (Test)69.1—Unverified
8NormTab+TabSQLifyAccuracy (Test)68.63—Unverified
9Chain-of-TableAccuracy (Test)67.31—Unverified
10Tab-PoTAccuracy (Test)66.78—Unverified
#ModelMetricClaimedVerifiedStatus
1RESDSQL-3B + NatSQLAccuracy84.1—Unverified
2code-davinci-002 175B (LEVER)Accuracy81.9—Unverified
3RASAT+PICARDAccuracy75.5—Unverified
4Graphix-3B + PICARDAccuracy74—Unverified
5T5-3B + PICARDAccuracy71.9—Unverified
6SADGA + GAPAccuracy70.1—Unverified
7RATSQL + GAPAccuracy69.7—Unverified
8RATSQL + Grammar-Augmented Pre-TrainingAccuracy69.6—Unverified
9RATSQL + BERTAccuracy65.6—Unverified
10Exact Set MatchingAccuracy19.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Dynamic Least-to-Most PromptingExact Match95—Unverified
2LeARExact Match90.9—Unverified
3T5-3B w/ Intermediate RepresentationsExact Match83.8—Unverified
4Hierarchical Poset DecodingExact Match69—Unverified
5Universal TransformerExact Match18.9—Unverified
#ModelMetricClaimedVerifiedStatus
1ReaRevAccuracy76.4—Unverified
2NSM+hAccuracy74.3—Unverified
3CBR-KBQAAccuracy70—Unverified
4STAGG (Yih et al., 2016)Accuracy63.9—Unverified
5T5-11B (Raffel et al., 2020)Accuracy56.5—Unverified
#ModelMetricClaimedVerifiedStatus
1CABINETDenotation accuracy (test)89.5—Unverified
2TAPEX-Large (weak supervision)Denotation accuracy (test)89.5—Unverified
3ReasTAP-Large (weak supervision)Denotation accuracy (test)89.2—Unverified
4NL2SQL-BERTAccuracy89—Unverified
5TAPAS-Large (weak supervision)Denotation accuracy (test)83.6—Unverified
#ModelMetricClaimedVerifiedStatus
1PhraseTransformerAccuracy90.4—Unverified
2TranxAccuracy86.2—Unverified
3ASN (Rabinovich et al., 2017)Accuracy85.3—Unverified
4ZH15 (Zhao and Huang, 2015)Accuracy84.2—Unverified
#ModelMetricClaimedVerifiedStatus
1coarse2fineAccuracy88.2—Unverified
2PhraseTransformerAccuracy87.9—Unverified
3TranxAccuracy87.7—Unverified
#ModelMetricClaimedVerifiedStatus
1PERIN + RobeCzechF192.36—Unverified
2PERINF192.24—Unverified
3HUJI-KUF158—Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF180.52—Unverified
2HUJI-KUF145—Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF180.23—Unverified
2HUJI-KUF152—Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF194.16—Unverified
2HUJI-KUF163—Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF189.83—Unverified
2HUJI-KUF162—Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF192.73—Unverified
2HUJI-KUF180—Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF189.19—Unverified
2HUJI-KUF154—Unverified
#ModelMetricClaimedVerifiedStatus
1TAPEX-LargeDenotation Accuracy74.5—Unverified
2TAPAS-LargeAccuracy67.2—Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF176.4—Unverified
2HUJI-KUF173—Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF181.01—Unverified
2HUJI-KUF175—Unverified
#ModelMetricClaimedVerifiedStatus
1HSPEM66.18—Unverified
#ModelMetricClaimedVerifiedStatus
1ReasonBERTRF1 Score41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1MeMCEExact40.3—Unverified