Semantic Parsing
381 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Semantic Parsing models and implementationsDatasets
FrameNet
ATIS
WikiSQL
SCAN
Spider-Realistic
WikiTableQuestions
Occluded REID
ComplexWebQuestions
Subtasks
Latest papers
Semantic Decomposition of Question and SQL for Text-to-SQL Parsing
However, this strategy encounters two major obstacles: (1) existing datasets lack question decomposition; (2) due to the syntactic complexity of SQL, most complex queries cannot be disentangled into sub-queries that can be readily recomposed.
MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations
Additionally, our analysis uncovers the semantic predispositions in LLMs and reveals the impact of recency bias for information presented in long contexts.
Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking
ToLightUpTheSky/ParsingDST
•
•
Zero-shot Dialogue State Tracking (DST) addresses the challenge of acquiring and annotating task-oriented dialogues, which can be time-consuming and costly.
ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models
lhrlab/chatkbqa
•
•
Knowledge Base Question Answering (KBQA) aims to derive answers to natural language questions over large-scale knowledge bases (KBs), which are generally divided into two research components: knowledge retrieval and semantic parsing.
An Investigation of LLMs' Inefficacy in Understanding Converse Relations
Our ConvRE features two tasks, Re2Text and Text2Re, which are formulated as multi-choice question answering to evaluate LLMs' ability to determine the matching between relations and associated text.
Towards End-User Development for IoT: A Case Study on Semantic Parsing of Cooking Recipes for Programming Kitchen Devices
Semantic parsing of user-generated instructional text, in the way of enabling end-users to program the Internet of Things (IoT), is an underexplored area.
Few-Shot Adaptation for Parsing Contextual Utterances with LLMs
We evaluate the ability of semantic parsers based on large language models (LLMs) to handle contextual utterances.
Data Distribution Bottlenecks in Grounding Language Models to Knowledge Bases
Language models (LMs) have already demonstrated remarkable abilities in understanding and generating both natural and formal language.
Code-Style In-Context Learning for Knowledge-Based Question Answering
Current methods for Knowledge-Based Question Answering (KBQA) usually rely on complex training techniques and model frameworks, leading to many limitations in practical applications.
ReCoMIF: Reading comprehension based multi-source information fusion network for Chinese spoken language understanding
It usually includes slot filling and intent detection (SFID) tasks aiming at semantic parsing of utterances.
