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
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | ARTEMIS-DA | Accuracy (Test) | 80.8 | — | Unverified |
| 2 | SynTQA (Oracle) | Test Accuracy | 77.5 | — | Unverified |
| 3 | TabLaP | Accuracy (Test) | 76.6 | — | Unverified |
| 4 | SynTQA (GPT) | Accuracy (Test) | 74.4 | — | Unverified |
| 5 | Mix SC | Accuracy (Test) | 73.6 | — | Unverified |
| 6 | SynTQA (RF) | Accuracy (Test) | 71.6 | — | Unverified |
| 7 | CABINET | Accuracy (Test) | 69.1 | — | Unverified |
| 8 | NormTab+TabSQLify | Accuracy (Test) | 68.63 | — | Unverified |
| 9 | Chain-of-Table | Accuracy (Test) | 67.31 | — | Unverified |
| 10 | Tab-PoT | Accuracy (Test) | 66.78 | — | Unverified |