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 1–10 of 1202 papers

TitleStatusHype
Where, What, Why: Towards Explainable Driver Attention PredictionCode1
Beyond Chains: Bridging Large Language Models and Knowledge Bases in Complex Question Answering—0
Creativity or Brute Force? Using Brainteasers as a Window into the Problem-Solving Abilities of Large Language Models—0
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 Graphs—0
Geo-Semantic-Parsing: AI-powered geoparsing by traversing semantic knowledge graphs—0
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 Rewriting—0
MCTS-KBQA: Monte Carlo Tree Search for Knowledge Base Question Answering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ARTEMIS-DAAccuracy (Test)80.8—Unverified
2SynTQA (Oracle)Test Accuracy77.5—Unverified
3TabLaPAccuracy (Test)76.6—Unverified
4SynTQA (GPT)Accuracy (Test)74.4—Unverified
5Mix SCAccuracy (Test)73.6—Unverified
6SynTQA (RF)Accuracy (Test)71.6—Unverified
7CABINETAccuracy (Test)69.1—Unverified
8NormTab+TabSQLifyAccuracy (Test)68.63—Unverified
9Chain-of-TableAccuracy (Test)67.31—Unverified
10Tab-PoTAccuracy (Test)66.78—Unverified