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 51100 of 1202 papers

TitleStatusHype
Language to Logical Form with Neural AttentionCode1
Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based ReasoningCode1
Learning from Executions for Semantic ParsingCode1
Learning Semantic Person Image Generation by Region-Adaptive NormalizationCode1
Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic ParsingCode1
AMR Parsing as Sequence-to-Graph TransductionCode1
Lexicon Learning for Few-Shot Neural Sequence ModelingCode1
Lexicon Learning for Few Shot Sequence ModelingCode1
Code-Style In-Context Learning for Knowledge-Based Question AnsweringCode1
CST5: Data Augmentation for Code-Switched Semantic ParsingCode1
NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating SystemCode1
DART: Open-Domain Structured Data Record to Text GenerationCode1
Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLsCode1
DPF: Learning Dense Prediction Fields with Weak SupervisionCode1
Evaluating Scoped Meaning RepresentationsCode1
Benchmarking Meaning Representations in Neural Semantic ParsingCode1
Bidirectional Attentive Memory Networks for Question Answering over Knowledge BasesCode1
BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic ParsingCode1
Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic ParsingCode1
Differentiable Tree Operations Promote Compositional GeneralizationCode1
Break It Down: A Question Understanding BenchmarkCode1
Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic ParsingCode1
CABINET: Content Relevance based Noise Reduction for Table Question AnsweringCode1
Calibrated Interpretation: Confidence Estimation in Semantic ParsingCode1
An Investigation of LLMs' Inefficacy in Understanding Converse RelationsCode1
Context Dependent Semantic Parsing: A SurveyCode1
CogIE: An Information Extraction Toolkit for Bridging Texts and CogNetCode1
Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene UnderstandingCode1
A Comprehensive Exploration on WikiSQL with Table-Aware Word ContextualizationCode1
COGS: A Compositional Generalization Challenge Based on Semantic InterpretationCode1
Compositional Exemplars for In-context LearningCode1
Complex Knowledge Base Question Answering: A SurveyCode1
A Survey on Non-Autoregressive Generation for Neural Machine Translation and BeyondCode1
3D-to-2D Distillation for Indoor Scene ParsingCode1
A Pilot Study of Text-to-SQL Semantic Parsing for VietnameseCode1
Few-Shot Semantic Parsing for New PredicatesCode1
Towards General Natural Language Understanding with Probabilistic WorldbuildingCode1
Compositional Semantic Parsing on Semi-Structured TablesCode1
GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate RepresentationCode1
ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question AnsweringCode1
Grounded Adaptation for Zero-shot Executable Semantic ParsingCode1
Human Parsing Based Texture Transfer from Single Image to 3D Human via Cross-View ConsistencyCode1
A Graph-Based Neural Model for End-to-End Frame Semantic ParsingCode1
Constrained Language Models Yield Few-Shot Semantic ParsersCode1
AIT-QA: Question Answering Dataset over Complex Tables in the Airline IndustryCode1
Contextual Semantic Parsing for Multilingual Task-Oriented DialoguesCode1
Abstract Meaning Representation Guided Graph Encoding and Decoding for Joint Information ExtractionCode1
Inducing Systematicity in Transformers by Attending to Structurally Quantized EmbeddingsCode1
Conversational Semantic Parsing for Dialog State TrackingCode1
AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training DataCode1
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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