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
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Latest papers
Improving Generalization in Semantic Parsing by Increasing Natural Language Variation
Text-to-SQL semantic parsing has made significant progress in recent years, with various models demonstrating impressive performance on the challenging Spider benchmark.
Inducing Systematicity in Transformers by Attending to Structurally Quantized Embeddings
Transformers generalize to novel compositions of structures and entities after being trained on a complex dataset, but easily overfit on datasets of insufficient complexity.
Evaluating Large Language Models in Semantic Parsing for Conversational Question Answering over Knowledge Graphs
Conversational question answering systems often rely on semantic parsing to enable interactive information retrieval, which involves the generation of structured database queries from a natural language input.
Rethinking Tabular Data Understanding with Large Language Models
Large Language Models (LLMs) have shown to be capable of various tasks, yet their capability in interpreting and reasoning over tabular data remains an underexplored area.
Leveraging Code to Improve In-context Learning for Semantic Parsing
In-context learning (ICL) is an appealing approach for semantic parsing due to its few-shot nature and improved generalization.
Weakly Supervised Semantic Parsing with Execution-based Spurious Program Filtering
The problem of spurious programs is a longstanding challenge when training a semantic parser from weak supervision.
SLOG: A Structural Generalization Benchmark for Semantic Parsing
The goal of compositional generalization benchmarks is to evaluate how well models generalize to new complex linguistic expressions.
A Unified View of Evaluation Metrics for Structured Prediction
We present a conceptual framework that unifies a variety of evaluation metrics for different structured prediction tasks (e. g. event and relation extraction, syntactic and semantic parsing).
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.