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 901950 of 1202 papers

TitleStatusHype
Automatically Tagging Constructions of Causation and Their Slot-Fillers0
Automatic classification of semantic patterns from the Pattern Dictionary of English Verbs0
AutoNLU: Detecting, root-causing, and fixing NLU model errors0
Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing0
Because Syntax Does Matter: Improving Predicate-Argument Structures Parsing with Syntactic Features0
Better Query Graph Selection for Knowledge Base Question Answering0
Better Transition-Based AMR Parsing with a Refined Search Space0
Beyond Chains: Bridging Large Language Models and Knowledge Bases in Complex Question Answering0
Beyond Sentential Semantic Parsing: Tackling the Math SAT with a Cascade of Tree Transducers0
Beyond Word Embeddings: Learning Entity and Concept Representations from Large Scale Knowledge Bases0
Biomedical Event Extraction Using Convolutional Neural Networks and Dependency Parsing0
Biphasic Face Photo-Sketch Synthesis via Semantic-Driven Generative Adversarial Network with Graph Representation Learning0
BMEAUT at SemEval-2020 Task 2: Lexical Entailment with Semantic Graphs0
BME-UW at SRST-2019: Surface realization with Interpreted Regular Tree Grammars0
Book Reviews: Ontology-Based Interpretation of Natural Language by Philipp Cimiano, Christina Unger and John McCrae0
Bootstrapping Multilingual Semantic Parsers using Large Language Models0
Bottom-Up Unranked Tree-to-Graph Transducers for Translation into Semantic Graphs0
Breeding Fillmore’s Chickens and Hatching the Eggs: Recombining Frames and Roles in Frame-Semantic Parsing0
Broad-coverage CCG Semantic Parsing with AMR0
Broad-Coverage Semantic Parsing as Transduction0
Building a Neural Semantic Parser from a Domain Ontology0
Building compositional semantics and higher-order inference system for a wide-coverage Japanese CCG parser0
Cache Transition Systems for Graph Parsing0
Case-based Reasoning for Natural Language Queries over Knowledge Bases0
ccg2lambda: A Compositional Semantics System0
*-CFQ: Analyzing the Scalability of Machine Learning on a Compositional Task0
CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsing0
Classifying Temporal Relations with Simple Features0
CMU: Arc-Factored, Discriminative Semantic Dependency Parsing0
Combining Formal and Distributional Models of Temporal and Intensional Semantics0
Combining Improvements for Exploiting Dependency Trees in Neural Semantic Parsing0
Comparing Knowledge-Intensive and Data-Intensive Models for English Resource Semantic Parsing0
Comparing Representations of Semantic Roles for String-To-Tree Decoding0
Compositional Generalization for Natural Language Interfaces to Web APIs0
Compositional Generalization for Neural Semantic Parsing via Span-level Supervised Attention0
Compositional Generalization in Dependency Parsing0
Compositional Generalization via Semantic Tagging0
Compositional Neural Machine Translation by Removing the Lexicon from Syntax0
Compositional pre-training for neural semantic parsing0
Compositional Semantic Parsing Across Graphbanks0
Compositional Semantic Parsing with Large Language Models0
Compressing Transformer-Based Semantic Parsing Models using Compositional Code Embeddings0
Computational linking theory0
Constrained Semantic Forests for Improved Discriminative Semantic Parsing0
Constructing Large Proposition Databases0
Constructing Web-Accessible Semantic Role Labels and Frames for Japanese as Additions to the NPCMJ Parsed Corpus0
Construction of an English Dependency Corpus incorporating Compound Function Words0
Context-Dependent Semantic Parsing for Temporal Relation Extraction0
Context-dependent Semantic Parsing for Time Expressions0
Context Dependent Semantic Parsing over Temporally Structured Data0
Show:102550
← PrevPage 19 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