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
Differentiable Tree Operations Promote Compositional GeneralizationCode1
Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic ParsingCode1
GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate RepresentationCode1
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL ParsersCode1
ReasonBERT: Pre-trained to Reason with Distant SupervisionCode1
Diverse Demonstrations Improve In-context Compositional GeneralizationCode1
ReCOGS: How Incidental Details of a Logical Form Overshadow an Evaluation of Semantic InterpretationCode1
Rethinking Tabular Data Understanding with Large Language ModelsCode1
DPF: Learning Dense Prediction Fields with Weak SupervisionCode1
Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic ParsingCode1
A Survey on Non-Autoregressive Generation for Neural Machine Translation and BeyondCode1
Evaluating statistical language models as pragmatic reasonersCode1
AMR Parsing as Sequence-to-Graph TransductionCode1
SmBoP: Semi-autoregressive Bottom-up Semantic ParsingCode1
Grammar Prompting for Domain-Specific Language Generation with Large Language ModelsCode1
SpCQL: A Semantic Parsing Dataset for Converting Natural Language into CypherCode1
Evaluating Scoped Meaning RepresentationsCode1
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
Exploring Neural Methods for Parsing Discourse Representation StructuresCode1
TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table DecompositionCode1
TAPEX: Table Pre-training via Learning a Neural SQL ExecutorCode1
Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word ProblemCode1
FeTaQA: Free-form Table Question AnsweringCode1
Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic ParsingCode1
The Natural Language Decathlon: Multitask Learning as Question AnsweringCode1
Break It Down: A Question Understanding BenchmarkCode1
Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic ScaffoldCode1
3D-to-2D Distillation for Indoor Scene ParsingCode1
Focusing on Persons: Colorizing Old Images Learning from Modern Historical MoviesCode1
CABINET: Content Relevance based Noise Reduction for Table Question AnsweringCode1
Calibrated Interpretation: Confidence Estimation in Semantic ParsingCode1
AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training DataCode1
Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLsCode1
Generating Data for Symbolic Language with Large Language ModelsCode1
Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERTCode1
BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic ParsingCode1
Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene UnderstandingCode1
Benchmarking Meaning Representations in Neural Semantic ParsingCode1
Code Generation from Natural Language with Less Prior Knowledge and More Monolingual DataCode1
CogIE: An Information Extraction Toolkit for Bridging Texts and CogNetCode1
GraPPa: Grammar-Augmented Pre-Training for Table Semantic ParsingCode1
Compositional Exemplars for In-context LearningCode1
Improved Semantic Role Labeling using Parameterized Neighborhood Memory AdaptationCode1
Improving Compositional Generalization with Latent Structure and Data AugmentationCode1
Bidirectional Attentive Memory Networks for Question Answering over Knowledge BasesCode1
Identity-Guided Human Semantic Parsing for Person Re-IdentificationCode1
Inducing Systematicity in Transformers by Attending to Structurally Quantized EmbeddingsCode1
Compositional Semantic Parsing on Semi-Structured TablesCode1
Learning Semantic Person Image Generation by Region-Adaptive NormalizationCode1
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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