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 201250 of 1202 papers

TitleStatusHype
A Study of using Syntactic and Semantic Structures for Concept Segmentation and Labeling0
Compositional Semantic Parsing with Large Language Models0
Aligning Formal Meaning Representations with Surface Strings for Wide-Coverage Text Generation0
Active learning for deep semantic parsing0
Deep Learning and Continuous Representations for Natural Language Processing0
Deep Semantic Parsing of Freehand Sketches with Homogeneous Transformation, Soft-Weighted Loss, and Staged Learning0
A Step-wise Usage-based Method for Inducing Polysemy-aware Verb Classes0
A Strong Lexical Matching Method for the Machine Comprehension Test0
Compressing Transformer-Based Semantic Parsing Models using Compositional Code Embeddings0
Computational linking theory0
A Study on the Integration of Pipeline and E2E SLU systems for Spoken Semantic Parsing toward STOP Quality Challenge0
ALB at SemEval-2018 Task 10: A System for Capturing Discriminative Attributes0
Constrained Semantic Forests for Improved Discriminative Semantic Parsing0
Constructing Large Proposition Databases0
Constructing Web-Accessible Semantic Role Labels and Frames for Japanese as Additions to the NPCMJ Parsed Corpus0
Construction of an English Dependency Corpus incorporating Compound Function Words0
A Survey of Syntactic-Semantic Parsing Based on Constituent and Dependency Structures0
Alto: Rapid Prototyping for Parsing and Translation0
A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions0
A Methodology for Creating Question Answering Corpora Using Inverse Data Annotation0
Compositional pre-training for neural semantic parsing0
Context-Dependent Semantic Parsing for Temporal Relation Extraction0
Compositional Neural Machine Translation by Removing the Lexicon from Syntax0
Context-dependent Semantic Parsing for Time Expressions0
Context Dependent Semantic Parsing over Temporally Structured Data0
A Survey on Semantic Parsing0
A Statistical Machine Translation Model with Forest-to-Tree Algorithm for Semantic Parsing0
Debugging Frame Semantic Role Labeling0
ConTFV: A Contrastive Learning Framework for Table-based Fact Verification0
Update Frequently, Update Fast: Retraining Semantic Parsing Systems in a Fraction of Time0
A Multi-layer LSTM-based Approach for Robot Command Interaction Modeling0
Assessing Data Efficiency in Task-Oriented Semantic Parsing0
Conversational Semantic Parsing0
Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL0
A Multimodal LDA Model integrating Textual, Cognitive and Visual Modalities0
Cooking with Semantics0
Copenhagen-Malm\"o: Tree Approximations of Semantic Parsing Problems0
AT\&T: The Tag\&Parse Approach to Semantic Parsing of Robot Spatial Commands0
A Two-Stage Approach towards Generalization in Knowledge Base Question Answering0
Corpus Creation and Language Identification in Low-Resource Code-Mixed Telugu-English Text0
A Two-Stage Approach towards Generalization in Knowledge Base Question Answering0
Accurate polyglot semantic parsing with DAG grammars0
A Type-coherent, Expressive Representation as an Initial Step to Language Understanding0
Counting What Deserves to be Counted for Graph Parsing0
Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing0
COVER: Covering the Semantically Tractable Questions0
A Knowledge-Guided Framework for Frame Identification0
Compositional Generalization via Semantic Tagging0
A Unified Framework for Discourse Argument Identification via Shallow Semantic Parsing0
Active Dialogue Simulation in Conversational Systems0
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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