Papers with Code 아카이브

Semantic Parsing

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 7
평가표: 20
보존된 평가 기록: 78

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

논문

이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

Semantic Parsing

논문

SQL-o1: Automatic Text-to-SQL Program Synthesis via Self-Reward Heuristic Search

CogniSQL-R1-Zero: Lightweight Reinforced Reasoning for Efficient SQL Generation

XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL

Where, What, Why: Towards Explainable Driver Attention Prediction

SWE-SQL: Illuminating LLM Pathways to Solve User SQL Issues in Real-World Applications

Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task

Bridging the Gap Between Open-Source and Proprietary LLMs in Table QA

LLM-Driven Data Generation and a Novel Soft Metric for Evaluating Text-to-SQL in Aviation MRO

HI-SQL: Optimizing Text-to-SQL Systems through Dynamic Hint Integration

SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation

SDE-SQL: Enhancing Text-to-SQL Generation in Large Language Models via Self-Driven Exploration with SQL Probes

SQLens: An End-to-End Framework for Error Detection and Correction in Text-to-SQL

Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning

Pi-SQL: Enhancing Text-to-SQL with Fine-Grained Guidance from Pivot Programming Languages

SHARE: An SLM-based Hierarchical Action CorREction Assistant for Text-to-SQL

Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities

Calibrating LLMs for Text-to-SQL Parsing by Leveraging Sub-clause Frequencies

DCG-SQL: Enhancing In-Context Learning for Text-to-SQL with Deep Contextual Schema Link Graph

Meta-aware Learning in text-to-SQL Large Language Model

Structuring the Unstructured: A Multi-Agent System for Extracting and Querying Financial KPIs and Guidance

LogicCat: A Chain-of-Thought Text-to-SQL Benchmark for Multi-Domain Reasoning Challenges

UNJOIN: Enhancing Multi-Table Text-to-SQL Generation via Schema Simplification

SchemaGraphSQL: Efficient Schema Linking with Pathfinding Graph Algorithms for Text-to-SQL on Large-Scale Databases

BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge Bases

LLM-Powered Agents for Navigating Venice's Historical Cadastre

ExeSQL: Self-Taught Text-to-SQL Models with Execution-Driven Bootstrapping for SQL Dialects

Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQL

Beyond Chains: Bridging Large Language Models and Knowledge Bases in Complex Question Answering

Cheaper, Better, Faster, Stronger: Robust Text-to-SQL without Chain-of-Thought or Fine-Tuning

JOLT-SQL: Joint Loss Tuning of Text-to-SQL with Confusion-aware Noisy Schema Sampling

ReEx-SQL: Reasoning with Execution-Aware Reinforcement Learning for Text-to-SQL

CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning

SQLForge: Synthesizing Reliable and Diverse Data to Enhance Text-to-SQL Reasoning in LLMs

Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward

Creativity or Brute Force? Using Brainteasers as a Window into the Problem-Solving Abilities of Large Language Models

Ambiguity Resolution in Text-to-Structured Data Mapping