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 751800 of 1202 papers

TitleStatusHype
Turku: Semantic Dependency Parsing as a Sequence Classification0
Type-Driven Incremental Semantic Parsing with Polymorphism0
UC Davis at SemEval-2019 Task 1: DAG Semantic Parsing with Attention-based Decoder0
UCL+Sheffield at SemEval-2016 Task 8: Imitation learning for AMR parsing with an alpha-bound0
\'UFAL-Oslo at MRP 2019: Garage Sale Semantic Parsing0
Un algorithme d’analyse sémantique fondée sur les graphes via le problème de l’arborescence généralisée couvrante (A graph-based semantic parsing algorithm via the generalized spanning arborescence problem)0
Unanimous Prediction for 100\% Precision with Application to Learning Semantic Mappings0
Uncertainty and Traffic-Aware Active Learning for Semantic Parsing0
Unfreeze with Care: Space-Efficient Fine-Tuning of Semantic Parsing Models0
Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database0
Universal Discourse Representation Structure Parsing0
Universal Semantic Annotator: the First Unified API for WSD, SRL and Semantic Parsing0
UnixMan Corpus: A Resource for Language Learning in the Unix Domain0
Unsupervised frame based Semantic Role Induction: application to French and English0
Unsupervised Induction of Frame-Semantic Representations0
Unsupervised Semantic Parsing of Video Collections0
Update Frequently, Update Fast: Retraining Semantic Parsing Systems in a Fraction of Time0
Using C5.0 and Exhaustive Search for Boosting Frame-Semantic Parsing Accuracy0
Using language technology resources and tools to construct Swedish FrameNet0
Using Positional Suffix Trees to Perform Agile Tree Kernel Calculation0
Using Semantic Unification to Generate Regular Expressions from Natural Language0
Using Shallow Semantic Parsing and Relation Extraction for Finding Contradiction in Text0
Usnea: An Authorship Tool for Interactive Fiction using Retrieval Based Semantic Parsing0
UWM: Applying an Existing Trainable Semantic Parser to Parse Robotic Spatial Commands0
Value-Agnostic Conversational Semantic Parsing0
Vector Space Semantic Parsing: A Framework for Compositional Vector Space Models0
Verb Classification using Distributional Similarity in Syntactic and Semantic Structures0
Visual Semantic Parsing: From Images to Abstract Meaning Representation0
Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions0
Weakly-supervised Neural Semantic Parsing with a Generative Ranker0
Weakly Supervised Semantic Parsing with Abstract Examples0
Weakly Supervised Training of Semantic Parsers0
Web Chat Conversations from Contact Centers: a Descriptive Study0
WebChild 2.0 : Fine-Grained Commonsense Knowledge Distillation0
Werdy: Recognition and Disambiguation of Verbs and Verb Phrases with Syntactic and Semantic Pruning0
What are You Talking About? Text-to-Image Coreference0
What Did You Say? Task-Oriented Dialog Datasets Are Not Conversational!?0
When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems0
WHUNlp at SemEval-2016 Task DiMSUM: A Pilot Study in Detecting Minimal Semantic Units and their Meanings using Supervised Models0
WikiBank: Using Wikidata to Improve Multilingual Frame-Semantic Parsing0
Word Level Language Identification in English Telugu Code Mixed Data0
Wordnet-Based Cross-Language Identification of Semantic Relations0
World Knowledge for Abstract Meaning Representation Parsing0
XJSA at SemEval-2017 Task 4: A Deep System for Sentiment Classification in Twitter0
Zero-shot Learning of Classifiers from Natural Language Quantification0
Zero-shot Transfer Learning for Semantic Parsing0
ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models0
CraftAssist Instruction Parsing: Semantic Parsing for a Minecraft Assistant0
ZOGRASCOPE: A New Benchmark for Property Graphs0
Jointly Learning Semantic Parser and Natural Language Generator via Dual Information Maximization0
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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