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 651700 of 1202 papers

TitleStatusHype
Discourse Representation Parsing for Sentences and Documents0
AdaNSP: Uncertainty-driven Adaptive Decoding in Neural Semantic Parsing0
Reranking for Neural Semantic Parsing0
Latent Structure Models for Natural Language Processing0
Compositional Semantic Parsing Across Graphbanks0
Semantic expressive capacity with bounded memory0
Program Synthesis and Semantic Parsing with Learned Code IdiomsCode0
Semantically Driven Auto-completion0
Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing0
Unified Semantic Parsing with Weak SupervisionCode0
Multimodal Logical Inference System for Visual-Textual Entailment0
SParC: Cross-Domain Semantic Parsing in ContextCode0
Jointly Learning Semantic Parser and Natural Language Generator via Dual Information Maximization0
DANGNT@UIT.VNU-HCM at SemEval 2019 Task 1: Graph Transformation System from Stanford Basic Dependencies to Universal Conceptual Cognitive Annotation (UCCA)0
Context Dependent Semantic Parsing over Temporally Structured Data0
UC Davis at SemEval-2019 Task 1: DAG Semantic Parsing with Attention-based Decoder0
Cross-lingual CCG InductionCode0
Content Differences in Syntactic and Semantic RepresentationCode0
Iterative Search for Weakly Supervised Semantic Parsing0
GCN-Sem at SemEval-2019 Task 1: Semantic Parsing using Graph Convolutional and Recurrent Neural Networks0
T\"upa at SemEval-2019 Task1: (Almost) feature-free Semantic Parsing0
Enhancing Key-Value Memory Neural Networks for Knowledge Based Question Answering0
CUNY-PKU Parser at SemEval-2019 Task 1: Cross-Lingual Semantic Parsing with UCCACode0
Grammar-based Neural Text-to-SQL Generation0
Compositional pre-training for neural semantic parsing0
Generating Logical Forms from Graph Representations of Text and Entities0
AMR Parsing as Sequence-to-Graph TransductionCode1
Questions in Dependent Type Semantics0
Proceedings of the IWCS Shared Task on Semantic Parsing0
Linguistic Information in Neural Semantic Parsing with Multiple Encoders0
Transition-based DRS Parsing Using Stack-LSTMs0
Context-Dependent Semantic Parsing over Temporally Structured DataCode0
Multi-Task Learning for Semantic Parsing with Cross-Domain Sketch0
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural SupervisionCode0
Good-Enough Compositional Data AugmentationCode0
Learning Programmatic Idioms for Scalable Semantic Parsing0
CraftAssist Instruction Parsing: Semantic Parsing for a Minecraft Assistant0
Unsupervised Person Image Generation with Semantic Parsing TransformationCode0
An Improved Coarse-to-Fine Method for Solving Generation Tasks0
A Pointer Network Architecture for Context-Dependent Semantic Parsing0
Domain Adaptation for Low-Resource Neural Semantic Parsing0
Neural Program Planner for Structured Predictions0
A Type-coherent, Expressive Representation as an Initial Step to Language Understanding0
Content Differences in Syntactic and Semantic RepresentationsCode0
HLT@SUDA at SemEval 2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing0
Practical Semantic Parsing for Spoken Language Understanding0
Bidirectional Attentive Memory Networks for Question Answering over Knowledge BasesCode1
SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA0
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over ParagraphsCode0
Learning to Generalize from Sparse and Underspecified RewardsCode0
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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