Semantic Parsing
280 papers with code • 19 benchmarks • 37 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
SPIDER
NomBank
CFQ
WikiTableQuestions
ComplexWebQuestions
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
google-research/text-to-text-transfer-transformer
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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.
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.
Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
crazydonkey200/neural-symbolic-machines
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NeurIPS 2018
We present Memory Augmented Policy Optimization (MAPO), a simple and novel way to leverage a memory buffer of promising trajectories to reduce the variance of policy gradient estimate.
