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 11511200 of 1202 papers

TitleStatusHype
Counterfactual Explanations for Natural Language InterfacesCode0
Correcting Semantic Parses with Natural Language through Dynamic Schema EncodingCode0
Cornell SPF: Cornell Semantic Parsing FrameworkCode0
CORD: A Consolidated Receipt Dataset for Post-OCR ParsingCode0
LP-LM: No Hallucinations in Question Answering with Logic ProgrammingCode0
Conversational Semantic Parsing using Dynamic Context GraphsCode0
Macro Grammars and Holistic Triggering for Efficient Semantic ParsingCode0
MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel InterpretationsCode0
ReCoMIF: Reading comprehension based multi-source information fusion network for Chinese spoken language understandingCode0
Semantic Tagging with Deep Residual NetworksCode0
Unanimous Prediction for 100% Precision with Application to Learning Semantic MappingsCode0
Reducing Model Churn: Stable Re-training of Conversational AgentsCode0
Training Naturalized Semantic Parsers with Very Little DataCode0
A Grammar-Based Structural CNN Decoder for Code GenerationCode0
XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic ParsingCode0
Training Heterogeneous Features in Sequence to Sequence Tasks: Latent Enhanced Multi-filter Seq2Seq ModelCode0
Measuring Alignment Bias in Neural Seq2Seq Semantic ParsersCode0
Underwater Robotics Semantic Parser AssistantCode0
A Double-Graph Based Framework for Frame Semantic ParsingCode0
Measuring Compositional Generalization: A Comprehensive Method on Realistic DataCode0
Memory Augmented Policy Optimization for Program Synthesis and Semantic ParsingCode0
Memory-Based Semantic ParsingCode0
Merging Weak and Active Supervision for Semantic ParsingCode0
Unified Semantic Parsing with Weak SupervisionCode0
Meta-Learning a Cross-lingual Manifold for Semantic ParsingCode0
A Probabilistic Generative Grammar for Semantic ParsingCode0
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over ParagraphsCode0
Content Differences in Syntactic and Semantic RepresentationsCode0
Modeling Label Correlations for Second-Order Semantic Dependency Parsing with Mean-Field InferenceCode0
Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question AnsweringCode0
Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic ParsingCode0
A Unified View of Evaluation Metrics for Structured PredictionCode0
Controllable Semantic Parsing via Retrieval AugmentationCode0
ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question AnsweringCode0
Don't paraphrase, detect! Rapid and Effective Data Collection for Semantic ParsingCode0
SeqZero: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot ModelsCode0
SGL: Speaking the Graph Languages of Semantic Parsing via Multilingual TranslationCode0
ShadowGNN: Graph Projection Neural Network for Text-to-SQL ParserCode0
Shift-Reduce Task-Oriented Semantic Parsing with Stack-TransformersCode0
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural SupervisionCode0
Sigma: A dataset for text-to-code semantic parsing with statistical analysisCode0
XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning RepresentationsCode0
Doing Natural Language Processing in A Natural Way: An NLP toolkit based on object-oriented knowledge base and multi-level grammar baseCode0
Transformer with Tree-order Encoding for Neural Program GenerationCode0
Simpler Context-Dependent Logical Forms via Model ProjectionsCode0
Multilingual Neural Semantic Parsing for Low-Resourced LanguagesCode0
Multilingual Semantic Parsing And Code-SwitchingCode0
Transforming Dependency Structures to Logical Forms for Semantic ParsingCode0
Sister Help: Data Augmentation for Frame-Semantic Role LabelingCode0
Multimodal Contextualized Semantic Parsing from SpeechCode0
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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