Semantic Parsing
383 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
Does Character-level Information Always Improve DRS-based Semantic Parsing?
In the experiments, we compare F1-scores by shuffling the order and randomizing character sequences after testing the performance of character-level information.
Differentiable Tree Operations Promote Compositional Generalization
psoulos/dtm
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To facilitate the learning of these symbolic sequences, we introduce a differentiable tree interpreter that compiles high-level symbolic tree operations into subsymbolic matrix operations on tensors.
Zero and Few-shot Semantic Parsing with Ambiguous Inputs
We attempt to address this shortcoming by introducing AmP, a framework, dataset, and challenge for translating ambiguous natural language to formal representations like logic and code.
Correcting Semantic Parses with Natural Language through Dynamic Schema Encoding
In addressing the task of converting natural language to SQL queries, there are several semantic and syntactic challenges.
Grammar Prompting for Domain-Specific Language Generation with Large Language Models
berlino/grammar-prompting
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NeurIPS 2023
Large language models (LLMs) can learn to perform a wide range of natural language tasks from just a handful of in-context examples.
Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based Techniques
dakingrai/ood-generalization-semantic-boundary-techniques
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Compositional and domain generalization present significant challenges in semantic parsing, even for state-of-the-art semantic parsers based on pre-trained language models (LMs).
Compositional Generalization without Trees using Multiset Tagging and Latent Permutations
Our model outperforms pretrained seq2seq models and prior work on realistic semantic parsing tasks that require generalization to longer examples.
Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms
osu-nlp-group/fl4semanticparsing
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By leveraging data from multiple clients, the FL paradigm can be especially beneficial for clients that have little training data to develop a data-hungry neural semantic parser on their own.
The Role of Output Vocabulary in T2T LMs for SPARQL Semantic Parsing
debayan/sparql-vocab-substitution
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In this work, we analyse the role of output vocabulary for text-to-text (T2T) models on the task of SPARQL semantic parsing.
Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata
By pairing our semantic parser with GPT-3, we combine verifiable results with qualified GPT-3 guesses to provide useful answers to 96% of the questions in dev.
