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 451500 of 1202 papers

TitleStatusHype
Multiple Tasks Integration: Tagging, Syntactic and Semantic Parsing as a Single Task0
Exploiting Definitions for Frame IdentificationCode0
Semantic Parsing of Disfluent Speech0
FeTaQA: Free-form Table Question AnsweringCode1
NL-EDIT: Correcting semantic parse errors through natural language interactionCode0
Context Transformer with Stacked Pointer Networks for Conversational Question Answering over Knowledge GraphsCode0
Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic ParsingCode1
Comparing Knowledge-Intensive and Data-Intensive Models for English Resource Semantic Parsing0
Semantic Parsing to Manipulate Relational Database For a Management System0
On Robustness of Neural Semantic Parsers0
Few-Shot Semantic Parsing for New PredicatesCode1
El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing0
Distilling Large Language Models into Tiny and Effective Students using pQRNN0
Teach me how to Label: Labeling Functions from Natural Language with Text-to-text TransformersCode1
Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision SignalsCode1
Modeling Global Semantics for Question Answering over Knowledge Bases0
Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic ParsingCode1
Code Generation from Natural Language with Less Prior and More Monolingual DataCode1
Recurrently Controlling a Recurrent Network with Recurrent Networks Controlled by More Recurrent Networks0
PhraseTransformer: Self-Attention using Local Context for Semantic ParsingCode0
Learning Better Structured Representations Using Low-rank Adaptive Label Smoothing0
Adaptive Self-training for Neural Sequence Labeling with Few Labels0
SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing0
Optimizing Deeper Transformers on Small DatasetsCode1
Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic ParsingCode1
Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-TrainingCode1
*-CFQ: Analyzing the Scalability of Machine Learning on a Compositional Task0
Human Pose Transfer by Adaptive Hierarchical DeformationCode1
Iterative Utterance Segmentation for Neural Semantic Parsing0
Tracking Interaction States for Multi-Turn Text-to-SQL Semantic ParsingCode0
Bifold and Semantic Reasoning for Pedestrian Behavior Prediction0
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering0
Machine Translation Aided Bilingual Data-to-Text Generation and Semantic Parsing0
The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task: Overview and Evaluation Results (WebNLG+ 2020)0
Transformer Semantic Parsing0
Predicting Coreference in Abstract Meaning Representations0
Semantic parsing with fuzzy meaning representations0
Multi-level Alignment Pretraining for Multi-lingual Semantic Parsing0
Point-of-Interest Oriented Question Answering with Joint Inference of Semantic Matching and Distance Correlation0
BMEAUT at SemEval-2020 Task 2: Lexical Entailment with Semantic Graphs0
Semantic Structural Decomposition for Neural Machine TranslationCode0
Human Parsing Based Texture Transfer from Single Image to 3D Human via Cross-View ConsistencyCode1
Improved Semantic Role Labeling using Parameterized Neighborhood Memory AdaptationCode1
Encoding Syntactic Constituency Paths for Frame-Semantic Parsing with Graph Convolutional Networks0
Sequence-Level Mixed Sample Data AugmentationCode1
Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERTCode1
Generating Synthetic Data for Task-Oriented Semantic Parsing with Hierarchical Representations0
Context Dependent Semantic Parsing: A SurveyCode1
ÚFAL at MRP 2020: Permutation-invariant Semantic Parsing in PERINCode1
Uncertainty and Traffic-Aware Active Learning for Semantic Parsing0
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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