Papers with Code 아카이브
Semantic Parsing
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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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/PriyankaDatar/NLP_Project_Modfications
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/PriyankaDatar/NLP_SQL_Bert
SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning
https://github.com/PriyankaDatar/NLP_SQL_Project
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/PriyankaDatar/NLP_project_BIDAF
SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning
https://github.com/Pro-v-7/code-generation
StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing
https://github.com/SBPL-Cruz/perch_pose_sampler
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/SY-Xuan/pointnet_paddle
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/SapienzaNLP/spring
https://github.com/ShadowShadowWong/update-pointnet-work
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Sharif-SLPL/t5-fa
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/SonuDileep/3-D-Object-Detection-using-PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/System-T/UniversalPropositions
Improved Semantic Role Labeling using Parameterized Neighborhood Memory Adaptation
https://github.com/THHHomas/mls
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Taeuk-Jang/pointcompletion
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/TooTouch/SPARTA
A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization
https://github.com/UKPLab/coling2018-graph-neural-networks-question-answering
Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering
https://github.com/VaanHUANG/CSCI5210HW1
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/WangYueFt/dgcnn
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/WavesUR/embedded_TSM
https://github.com/XGenerationLab/M-Schema
A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL
https://github.com/XGenerationLab/XiYan-DateResolver
A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL
https://github.com/YanWei123/PointNet-encoder-and-FoldingNet-decoder-add-quantization-change-latent-code-size-from-512-to-1024
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/YanWei123/Pointnet_encoder_Foldingnet_decoder_quantization
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/YanWei123/Pytorch-implementation-of-FoldingNet-encoder-and-decoder-with-graph-pooling-covariance-add-quanti
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Yang2446/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Yijunmaverick/GenerativeFaceCompletion
https://github.com/YiruS/pointnet_adversarial
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Young98CN/pointconv_pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Yuto0107/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ZhihaoZhu/PointNet-Implementation-Tensorflow
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/abdullahozer11/Segmentation-and-Classification-of-Objects-in-Point-Clouds
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/abelriboulot/onnxt5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/abhishekchugh17/sql12
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/ag-sc/AMUSE
AMUSE: Multilingual Semantic Parsing for Question Answering over Linked Data
https://github.com/ahmed-anas/thesis-pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.