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 11011150 of 1202 papers

TitleStatusHype
Unveiling the Black Box of PLMs with Semantic Anchors: Towards Interpretable Neural Semantic Parsing0
Hierarchical Neural Data Synthesis for Semantic Parsing0
Hierarchical Poset Decoding for Compositional Generalization in Language0
HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing0
HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing0
Hinting Semantic Parsing with Statistical Word Sense Disambiguation0
HLT@SUDA at SemEval 2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing0
HopPG: Self-Iterative Program Generation for Multi-Hop Question Answering over Heterogeneous Knowledge0
How Does Code Pretraining Affect Language Model Task Performance?0
How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Context0
How well do Computers Solve Math Word Problems? Large-Scale Dataset Construction and Evaluation0
How Would You Say It? Eliciting Lexically Diverse Dialogue for Supervised Semantic Parsing0
Huge Automatically Extracted Training-Sets for Multilingual Word SenseDisambiguation0
HUJI-KU at MRP~2020: Two Transition-based Neural Parsers0
Human Semantic Parsing for Person Re-identification0
HuRIC: a Human Robot Interaction Corpus0
Hybrid Question Answering over Knowledge Base and Free Text0
Hyperedge Replacement and Nonprojective Dependency Structures0
ICT:A System Combination for Chinese Semantic Dependency Parsing0
Identifying Pathological Findings in German Radiology Reports Using a Syntacto-semantic Parsing Approach0
Identifying Various Kinds of Event Mentions in K-Parser Output0
IHS-RD-Belarus at SemEval-2016 Task 9: Transition-based Chinese Semantic Dependency Parsing with Online Reordering and Bootstrapping.0
Imitation learning for structured prediction in natural language processing0
ImPaKT: A Dataset for Open-Schema Knowledge Base Construction0
Improved Semantic Parsers For If-Then Statements0
Improving Black-box Speech Recognition using Semantic Parsing0
Improving Company Valuations with Automated Knowledge Discovery, Extraction and Fusion0
Improving Generalization in Semantic Parsing by Increasing Natural Language Variation0
Improving machine translation by training against an automatic semantic frame based evaluation metric0
Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning0
Improving Semantic Dependency Parsing with Syntactic Features0
Improving Semantic Parsing for Task Oriented Dialog0
Improving Semantic Parsing via Answer Type Inference0
Improving Semantic Parsing with Enriched Synchronous Context-Free Grammar0
Improving Semantic Parsing with Neural Generator-Reranker Architecture0
Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding0
Improving Top-K Decoding for Non-Autoregressive Semantic Parsing via Intent Conditioning0
Improving word alignment for low resource languages using English monolingual SRL0
Incorporating Compositionality and Morphology into End-to-End Models0
Incorporating Deep Syntactic and Semantic Knowledge for Chinese Sequence Labeling with GCN0
Indexicals and Compositionality: Inside-Out or Outside-In?0
Inducing Example-based Semantic Frames from a Massive Amount of Verb Uses0
Inducing Implicit Arguments from Comparable Texts: A Framework and Its Applications0
Information Extraction over Structured Data: Question Answering with Freebase0
Integer Linear Programming formulations in Natural Language Processing0
Integrated Learning of Dialog Strategies and Semantic Parsing0
Integrating Generative Lexicon Event Structures into VerbNet0
Intégration de tâches: étiquetage morpho-syntaxique, analyse syntaxique et analyse sémantique traités comme une tâche unique (Multiple Tasks Integration: Tagging, Syntactic and Semantic Parsing as a Single Task )0
Integrative Semantic Dependency Parsing via Efficient Large-scale Feature Selection0
Interactive Learning from Natural Language and Demonstrations using Signal Temporal Logic0
Show:102550
← PrevPage 23 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