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 110 of 1202 papers

TitleStatusHype
Where, What, Why: Towards Explainable Driver Attention PredictionCode1
Beyond Chains: Bridging Large Language Models and Knowledge Bases in Complex Question Answering0
Creativity or Brute Force? Using Brainteasers as a Window into the Problem-Solving Abilities of Large Language Models0
Sigma: A dataset for text-to-code semantic parsing with statistical analysisCode0
Diverse In-Context Example Selection After Decomposing Programs and Aligned Utterances Improves Semantic ParsingCode0
ZOGRASCOPE: A New Benchmark for Property Graphs0
Geo-Semantic-Parsing: AI-powered geoparsing by traversing semantic knowledge graphs0
Disambiguate First Parse Later: Generating Interpretations for Ambiguity Resolution in Semantic ParsingCode0
ReVision: A Dataset and Baseline VLM for Privacy-Preserving Task-Oriented Visual Instruction Rewriting0
MCTS-KBQA: Monte Carlo Tree Search for Knowledge Base Question Answering0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PhraseTransformerAccuracy90.4Unverified
2TranxAccuracy86.2Unverified
3ASN (Rabinovich et al., 2017)Accuracy85.3Unverified
4ZH15 (Zhao and Huang, 2015)Accuracy84.2Unverified