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 251300 of 1202 papers

TitleStatusHype
Optimal Transport Posterior Alignment for Cross-lingual Semantic ParsingCode0
Incorporating Graph Information in Transformer-based AMR ParsingCode0
On Evaluating Multilingual Compositional Generalization with Translated DatasetsCode0
Discourse Representation Structure Parsing for ChineseCode0
T5-SR: A Unified Seq-to-Seq Decoding Strategy for Semantic ParsingCode0
Semantic Parsing of Colonoscopy Videos with Multi-Label Temporal Networks0
XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning RepresentationsCode0
Does Character-level Information Always Improve DRS-based Semantic Parsing?Code0
Incorporating Deep Syntactic and Semantic Knowledge for Chinese Sequence Labeling with GCN0
Zero and Few-shot Semantic Parsing with Ambiguous InputsCode0
Correcting Semantic Parses with Natural Language through Dynamic Schema EncodingCode0
Compositional Generalization without Trees using Multiset Tagging and Latent PermutationsCode0
Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New AlgorithmsCode0
The Role of Output Vocabulary in T2T LMs for SPARQL Semantic ParsingCode0
Measuring and Mitigating Constraint Violations of In-Context Learning for Utterance-to-API Semantic Parsing0
Error Detection for Text-to-SQL Semantic ParsingCode0
Skill-Based Few-Shot Selection for In-Context Learning0
The Best of Both Worlds: Combining Human and Machine Translations for Multilingual Semantic Parsing with Active Learning0
HPE:Answering Complex Questions over Text by Hybrid Question Parsing and Execution0
QURG: Question Rewriting Guided Context-Dependent Text-to-SQL Semantic Parsing0
Language Independent Neuro-Symbolic Semantic Parsing for Form UnderstandingCode0
Laziness Is a Virtue When It Comes to Compositionality in Neural Semantic Parsing0
Towards Zero-Shot Frame Semantic Parsing with Task Agnostic Ontologies and Simple Labels0
Conversational Semantic Parsing using Dynamic Context GraphsCode0
A Study on the Integration of Pipeline and E2E SLU systems for Spoken Semantic Parsing toward STOP Quality Challenge0
Evaluating Inter-Bilingual Semantic Parsing for Indian LanguagesCode0
Divide and Prompt: Chain of Thought Prompting for Text-to-SQL0
TreePiece: Faster Semantic Parsing via Tree Tokenization0
Did You Mean...? Confidence-based Trade-offs in Semantic Parsing0
On Codex Prompt Engineering for OCL Generation: An Empirical Study0
Knowledge-augmented Frame Semantic Parsing with Hybrid Prompt-tuning0
NLP Workbench: Efficient and Extensible Integration of State-of-the-art Text Mining ToolsCode0
Is Japanese CCGBank empirically correct? A case study of passive and causative constructions0
PAC Prediction Sets for Large Language Models of CodeCode0
On graph-based reentrancy-free semantic parsing0
The Role of Semantic Parsing in Understanding Procedural TextCode0
On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codex0
Active Learning for Multilingual Semantic Parser0
Underwater Robotics Semantic Parser AssistantCode0
Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification0
Explaining Large Language Model-Based Neural Semantic Parsers (Student Abstract)0
Low-Resource Compositional Semantic Parsing with Concept Pretraining0
Semantic-aware Contrastive Learning for More Accurate Semantic Parsing0
Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL ParsingCode0
Structured Case-based Reasoning for Inference-time Adaptation of Text-to-SQL parsers0
Towards Autoformalization of Mathematics and Code Correctness: Experiments with Elementary ProofsCode0
Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge0
Semantic Operator Prediction and Applications0
MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing0
ImPaKT: A Dataset for Open-Schema Knowledge Base Construction0
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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