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 551600 of 1202 papers

TitleStatusHype
Usnea: An Authorship Tool for Interactive Fiction using Retrieval Based Semantic Parsing0
Learning Web-based Procedures by Reasoning over Explanations and Demonstrations in Context0
Exploring Unexplored Generalization Challenges for Cross-Database Semantic Parsing0
Hinting Semantic Parsing with Statistical Word Sense Disambiguation0
Domain Adaptation for Semantic Parsing0
A Survey of Syntactic-Semantic Parsing Based on Constituent and Dependency Structures0
SPARQL query generation for complex question answering with BERT and BiLSTM-based model0
Analyse s\'emantique robuste par apprentissage antagoniste pour la g\'en\'eralisation de domaine (Robust Semantic Parsing with Adversarial Learning for Domain Generalization )0
Noise Robust Named Entity Understanding for Voice Assistants0
The First Shared Task on Discourse Representation Structure Parsing0
Unsupervised Dual Paraphrasing for Two-stage Semantic ParsingCode1
Thirty Musts for Meaning Banking0
TaBERT: Pretraining for Joint Understanding of Textual and Tabular DataCode1
DRTS Parsing with Structure-Aware Encoding and Decoding0
Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback0
Development of a General-Purpose Categorial Grammar Treebank0
Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing0
Constructing Web-Accessible Semantic Role Labels and Frames for Japanese as Additions to the NPCMJ Parsed Corpus0
WikiBank: Using Wikidata to Improve Multilingual Frame-Semantic Parsing0
Syntactic Question Abstraction and Retrieval for Data-Scarce Semantic Parsing0
Normalizing Compositional Structures Across Graphbanks0
LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network0
AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot FillingCode1
Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation0
A Methodology for Creating Question Answering Corpora Using Inverse Data Annotation0
Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene UnderstandingCode1
Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word ProblemCode1
Bootstrapping a Crosslingual Semantic ParserCode0
TAPAS: Weakly Supervised Table Parsing via Pre-trainingCode0
SPARQA: Skeleton-based Semantic Parsing for Complex Questions over Knowledge BasesCode1
Revisit Systematic Generalization via Meaningful LearningCode0
Exploring Neural Models for Parsing Natural Language into First-Order Logic0
A Data Efficient End-To-End Spoken Language Understanding Architecture0
Compositional Neural Machine Translation by Removing the Lexicon from Syntax0
How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Context0
Break It Down: A Question Understanding BenchmarkCode1
Don't Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing0
Schema2QA: High-Quality and Low-Cost Q&A Agents for the Structured WebCode1
From Natural Language Instructions to Complex Processes: Issues in Chaining Trigger Action Rules0
Weakly Supervised Visual Semantic ParsingCode1
Metagross: Meta Gated Recursive Controller Units for Sequence Modeling0
Measuring Compositional Generalization: A Comprehensive Method on Realistic DataCode0
Fast Intent Classification for Spoken Language UnderstandingCode0
Novel positional encodings to enable tree-based transformersCode0
Merging Weak and Active Supervision for Semantic ParsingCode0
TreeGen: A Tree-Based Transformer Architecture for Code GenerationCode1
Zero-Shot Semantic Parsing for InstructionsCode0
Data-Free Point Cloud Network for 3D Face Recognition0
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL ParsersCode1
Learning from Explanations with Neural Execution TreeCode0
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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