Semantic Parsing
380 papers with code • 20 benchmarks • 42 datasets
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
Benchmarks
These leaderboards are used to track progress in Semantic Parsing
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Libraries
Use these libraries to find Semantic Parsing models and implementationsDatasets
FrameNet
ATIS
WikiSQL
SCAN
Spider-Realistic
WikiTableQuestions
Occluded REID
ComplexWebQuestions
Subtasks
Latest papers with no code
Gaining More Insight into Neural Semantic Parsing with Challenging Benchmarks
The Parallel Meaning Bank (PMB) serves as a corpus for semantic processing with a focus on semantic parsing and text generation.
Self-Improvement Programming for Temporal Knowledge Graph Question Answering
Temporal Knowledge Graph Question Answering (TKGQA) aims to answer questions with temporal intent over Temporal Knowledge Graphs (TKGs).
QueryAgent: A Reliable and Efficient Reasoning Framework with Environmental Feedback based Self-Correction
Employing Large Language Models (LLMs) for semantic parsing has achieved remarkable success.
Interactive-KBQA: Multi-Turn Interactions for Knowledge Base Question Answering with Large Language Models
For each category of complex question, we devised exemplars to guide LLMs through the reasoning processes.
Ar-Spider: Text-to-SQL in Arabic
The baselines demonstrate decent single-language performance on our Arabic text-to-SQL dataset, Ar-Spider, achieving 62. 48% for S2SQL and 65. 57% for LGESQL, only 8. 79% below the highest results achieved by the baselines when trained in English dataset.
Training Table Question Answering via SQL Query Decomposition
Table Question-Answering involves both understanding the natural language query and grounding it in the context of the input table to extract the relevant information.
Neural Models for Source Code Synthesis and Completion
In this master thesis, we present sequence-to-sequence deep learning models and training paradigms to map NL to general-purpose programming languages that can assist users with suggestions of source code snippets, given a NL intent, and also extend auto-completion functionality of the source code to users while they are writing source code.
LB-KBQA: Large-language-model and BERT based Knowledge-Based Question and Answering System
The natural language understanding capability has always been a barrier to the intent recognition performance of the Knowledge-Based-Question-and-Answer (KBQA) system, which arises from linguistic diversity and the newly appeared intent.
Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
We propose the Chain-of-Table framework, where tabular data is explicitly used in the reasoning chain as a proxy for intermediate thoughts.
Semantic Parsing for Complex Data Retrieval: Targeting Query Plans vs. SQL for No-Code Access to Relational Databases
In this paper, we investigate the potential of an alternative query language with simpler syntax and modular specification of complex queries.
