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 10511100 of 1202 papers

TitleStatusHype
Data Recombination for Neural Semantic ParsingCode0
Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal KnowledgeCode0
Cross-lingual AMR Aligner: Paying Attention to Cross-AttentionCode0
OPERA: Operation-Pivoted Discrete Reasoning over TextCode0
Joint Universal Syntactic and Semantic ParsingCode0
Optimal Transport Posterior Alignment for Cross-lingual Semantic ParsingCode0
KaFSP: Knowledge-Aware Fuzzy Semantic Parsing for Conversational Question Answering over a Large-Scale Knowledge BaseCode0
Data Distribution Bottlenecks in Grounding Language Models to Knowledge BasesCode0
Overcoming Conflicting Data when Updating a Neural Semantic ParserCode0
PAC Prediction Sets for Large Language Models of CodeCode0
Vietnamese Semantic Role LabellingCode0
TurkishDelightNLP: A Neural Turkish NLP ToolkitCode0
Symbolic Priors for RNN-based Semantic ParsingCode0
Knowledge Base Question Answering via Encoding of Complex Query GraphsCode0
Revisit Systematic Generalization via Meaningful LearningCode0
X2Parser: Cross-Lingual and Cross-Domain Framework for Task-Oriented Compositional Semantic ParsingCode0
Parsing All: Syntax and Semantics, Dependencies and SpansCode0
Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical TextCode0
Unsupervised Person Image Generation with Semantic Parsing TransformationCode0
Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both?Code0
Syntax-aware Neural Semantic Role LabelingCode0
Extend, don’t rebuild: Phrasing conditional graph modification as autoregressive sequence labellingCode0
Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence ModelCode0
Syntax for Semantic Role Labeling, To Be, Or Not To BeCode0
Pay More Attention to History: A Context Modelling Strategy for Conversational Text-to-SQLCode0
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL TaskCode0
Language Independent Neuro-Symbolic Semantic Parsing for Form UnderstandingCode0
Towards End-User Development for IoT: A Case Study on Semantic Parsing of Cooking Recipes for Programming Kitchen DevicesCode0
Exploiting Definitions for Frame IdentificationCode0
Event Detection and Domain Adaptation with Convolutional Neural NetworksCode0
SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQACode0
PhraseTransformer: An Incorporation of Local Context Information into Sequence-to-sequence Semantic ParsingCode0
PhraseTransformer: Self-Attention using Local Context for Semantic ParsingCode0
DAMP: Doubly Aligned Multilingual Parser for Task-Oriented DialogueCode0
Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural NetworkCode0
CUNY-PKU Parser at SemEval-2019 Task 1: Cross-Lingual Semantic Parsing with UCCACode0
Cross-linguistically Consistent Semantic and Syntactic Annotation of Child-directed SpeechCode0
Compositional Generalisation with Structured Reordering and Fertility LayersCode0
Zero-Shot Semantic Parsing for InstructionsCode0
Polyglot Semantic Parsing in APIsCode0
When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding SystemsCode0
Learning Algebraic Recombination for Compositional GeneralizationCode0
T5-SR: A Unified Seq-to-Seq Decoding Strategy for Semantic ParsingCode0
Complex Question Decomposition for Semantic ParsingCode0
Predicate Representations and Polysemy in VerbNet Semantic ParsingCode0
Evaluating Structural Generalization in Neural Machine TranslationCode0
Table2answer: Read the database and answer without SQLCode0
Learning Dependency-Based Compositional SemanticsCode0
Coarse-to-Fine Decoding for Neural Semantic ParsingCode0
Evaluating Semantic Parsing against a Simple Web-based Question Answering ModelCode0
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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