Semantic Parsing
358 papers with code • 20 benchmarks • 40 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
HotpotQA
ATIS
WikiSQL
SCAN
CFQ
Occluded REID
NomBank
ComplexWebQuestions
WikiTableQuestions
Subtasks
Most implemented papers
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
charlesq34/pointnet
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CVPR 2017
Point cloud is an important type of geometric data structure.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
huggingface/transformers
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arXiv 2019
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP).
Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold
We present a new, efficient frame-semantic parser that labels semantic arguments to FrameNet predicates.
A Syntactic Neural Model for General-Purpose Code Generation
We consider the problem of parsing natural language descriptions into source code written in a general-purpose programming language like Python.
StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing
pcyin/tranX
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ACL 2018
Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures.
Language to Logical Form with Neural Attention
donglixp/lang2logic
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ACL 2016
Semantic parsing aims at mapping natural language to machine interpretable meaning representations.
The Natural Language Decathlon: Multitask Learning as Question Answering
salesforce/decaNLP
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ICLR 2019
Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting.
A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization
naver/sqlova
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We present SQLova, the first Natural-language-to-SQL (NL2SQL) model to achieve human performance in WikiSQL dataset.
SParC: Cross-Domain Semantic Parsing in Context
ryanzhumich/editsql
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ACL 2019
The best model obtains an exact match accuracy of 20. 2% over all questions and less than10% over all interaction sequences, indicating that the cross-domain setting and the con-textual phenomena of the dataset present significant challenges for future research.
TAPAS: Weakly Supervised Table Parsing via Pre-training
google-research/tapas
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ACL 2020
In this paper, we present TAPAS, an approach to question answering over tables without generating logical forms.
