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 301350 of 1202 papers

TitleStatusHype
Unobserved Local Structures Make Compositional Generalization HardCode0
A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge BasesCode0
Biphasic Face Photo-Sketch Synthesis via Semantic-Driven Generative Adversarial Network with Graph Representation Learning0
OpenQA: Hybrid QA System Relying on Structured Knowledge Base as well as Non-structured Data0
Combining Improvements for Exploiting Dependency Trees in Neural Semantic Parsing0
Pay More Attention to History: A Context Modelling Strategy for Conversational Text-to-SQLCode0
Few-Shot Semantic Parsing with Language Models Trained On Code0
Learning to Transpile AMR into SPARQL0
Few-shot Multi-hop Question Answering over Knowledge Base0
Improving Compositional Generalization with Latent Structure and Data AugmentationCode1
Compositional Generalization for Natural Language Interfaces to Web APIs0
Hierarchical Neural Data Synthesis for Semantic Parsing0
Context-Dependent Semantic Parsing for Temporal Relation Extraction0
Cross-lingual Alignment of Knowledge Graph Triples with Sentences0
Systematic Generalization with Edge TransformersCode1
Linking-Enhanced Pre-Training for Table Semantic Parsing0
When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems0
Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction0
LAGr: Label Aligned Graphs for Better Systematic Generalization in Semantic Parsing0
The Power of Prompt Tuning for Low-Resource Semantic Parsing0
Making Transformers Solve Compositional Tasks0
Active Dialogue Simulation in Conversational Systems0
Towards Collaborative Neural-Symbolic Graph Semantic Parsing via Uncertainty0
FedParsing: a Semi-Supervised Federated Learning Model on Semantic Parsing0
S^2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers0
Disentangled Sequence to Sequence Learning for Compositional Generalization0
HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing0
Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation0
CST5: Data augmentation for Code-Switched Semantic Parsing0
A Neural Approach to KGQA via SPARQL Silhouette Generation0
Counting What Deserves to be Counted for Graph Parsing0
ConTFV: A Contrastive Learning Framework for Table-based Fact Verification0
A Chinese Multi-type Complex Questions Answering Dataset over Wikidata0
A Two-Stage Approach towards Generalization in Knowledge Base Question Answering0
Learning to Generalize Compositionally by Transferring Across Semantic Parsing TasksCode0
Contextual Semantic Parsing for Multilingual Task-Oriented DialoguesCode1
An in-depth look at Euclidean disk embeddings for structure preserving parsing0
Extend, don’t rebuild: Phrasing conditional graph modification as autoregressive sequence labellingCode0
Frustratingly Simple but Surprisingly Strong: Using Language-Independent Features for Zero-shot Cross-lingual Semantic ParsingCode0
Few-Shot Novel Concept Learning for Semantic Parsing0
Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing0
Weakly Supervised Semantic Parsing by Learning from MistakesCode0
SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQLCode1
Findings from Experiments of On-line Joint Reinforcement Learning of Semantic Parser and Dialogue Manager with real Users0
A Double-Graph Based Framework for Frame Semantic Parsing0
Controllable Semantic Parsing via Retrieval AugmentationCode0
The Power of Prompt Tuning for Low-Resource Semantic Parsing0
On The Ingredients of an Effective Zero-shot Semantic Parser0
Towards Transparent Interactive Semantic Parsing via Step-by-Step CorrectionCode0
LAGr: Labeling Aligned Graphs for Improving Systematic Generalization in Semantic ParsingCode1
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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