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 251300 of 1202 papers

TitleStatusHype
A Grammar-Based Structural CNN Decoder for Code GenerationCode0
Learning from Explanations with Neural Execution TreeCode0
Cross-lingual Back-Parsing: Utterance Synthesis from Meaning Representation for Zero-Resource Semantic ParsingCode0
Cross-lingual CCG InductionCode0
Leveraging Code to Improve In-context Learning for Semantic ParsingCode0
Coarse-to-Fine Decoding for Neural Semantic ParsingCode0
A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge BasesCode0
Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract ProgramsCode0
Learning Joint Semantic Parsers from Disjoint DataCode0
A Probabilistic Generative Grammar for Semantic ParsingCode0
Learning Language Games through InteractionCode0
Learning Neural Sequence-to-Sequence Models from Weak Feedback with Bipolar Ramp LossCode0
Learning Algebraic Recombination for Compositional GeneralizationCode0
ccg2lambda: A Compositional Semantics SystemCode0
A Parser for LTAG and Frame SemanticsCode0
Language Independent Neuro-Symbolic Semantic Parsing for Form UnderstandingCode0
CAE: Mechanism to Diminish the Class Imbalanced in SLU Slot Filling TaskCode0
KaFSP: Knowledge-Aware Fuzzy Semantic Parsing for Conversational Question Answering over a Large-Scale Knowledge BaseCode0
Knowledge Base Question Answering via Encoding of Complex Query GraphsCode0
Butterfly Effects in Frame Semantic Parsing: impact of data processing on model rankingCode0
A Double-Graph Based Framework for Frame Semantic ParsingCode0
Building compositional semantics and higher-order inference system for a wide-coverage Japanese CCG parserCode0
Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement LearningCode0
Interactive Instance-based Evaluation of Knowledge Base Question AnsweringCode0
Joint Universal Syntactic and Semantic ParsingCode0
Learning Dependency-Based Compositional SemanticsCode0
Improving Generalization in Semantic Parsing by Increasing Natural Language VariationCode0
Imitation Learning of Agenda-based Semantic ParsersCode0
Bootstrapping a Crosslingual Semantic ParserCode0
Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit FeedbackCode0
Incorporating Graph Information in Transformer-based AMR ParsingCode0
Handling Ontology Gaps in Semantic ParsingCode0
Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMsCode0
Holistic Exploration on Universal Decompositional Semantic Parsing: Architecture, Data Augmentation, and LLM ParadigmCode0
Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL ParsingCode0
Good-Enough Compositional Data AugmentationCode0
How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in ContextCode0
Generative Face CompletionCode0
Generic Axiomatization of Families of Noncrossing Graphs in Dependency ParsingCode0
Function Assistant: A Tool for NL Querying of APIsCode0
Fully-Semantic Parsing and Generation: the BabelNet Meaning RepresentationCode0
Generic Axiomatization of Families of Noncrossing Graphs in Dependency ParsingCode0
From Characters to Time Intervals: New Paradigms for Evaluation and Neural Parsing of Time NormalizationsCode0
From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal LikelihoodCode0
Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional GeneralizationCode0
Frame- and Entity-Based Knowledge for Common-Sense Argumentative ReasoningCode0
Frustratingly Simple but Surprisingly Strong: Using Language-Independent Features for Zero-shot Cross-lingual Semantic ParsingCode0
Global Reasoning over Database Structures for Text-to-SQL ParsingCode0
Incorporating Lexical and Syntactic Knowledge for Unsupervised Cross-Lingual TransferCode0
Fast Intent Classification for Spoken Language UnderstandingCode0
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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