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 on Graphs for Natural Language Processing0
Compositional Semantic Parsing Across Graphbanks0
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
Compositional pre-training for neural semantic parsing0
Constrained Semantic Forests for Improved Discriminative Semantic Parsing0
Constructing Large Proposition Databases0
Compositional Neural Machine Translation by Removing the Lexicon from Syntax0
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
A Statistical Machine Translation Model with Forest-to-Tree Algorithm for Semantic Parsing0
Context-Dependent Semantic Parsing for Temporal Relation Extraction0
ALB at SemEval-2018 Task 10: A System for Capturing Discriminative Attributes0
Context-dependent Semantic Parsing for Time Expressions0
Context Dependent Semantic Parsing over Temporally Structured Data0
A Survey on Semantic Parsing0
Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing0
Assessing Data Efficiency in Task-Oriented Semantic Parsing0
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
Data Synthesis and Iterative Refinement for Neural Semantic Parsing without Annotated Logical Forms0
Conversational Semantic Parsing0
A Knowledge-Guided Framework for Frame Identification0
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
CraftAssist Instruction Parsing: Semantic Parsing for a Voxel-World Assistant0
Augmenting text for spoken language understanding with Large Language Models0
A Unified Framework for Discourse Argument Identification via Shallow Semantic Parsing0
Compositional Generalization via Semantic Tagging0
Show:102550
← PrevPage 5 of 25Next →

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