https://github.com/AlisonYao/NLP-Final-Project
TableQA: a Large-Scale Chinese Text-to-SQL Dataset for Table-Aware SQL Generation
Papers with Code 아카이브
Semantic Parsing
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
- 20
- 보존된 평가 기록
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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/AlvesRobot/Deep-Learning-on-Point-Cloud-for-3D-Classification-and-Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/AndreiMoraru123/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Aprilkaka/nonlocal_mindspore
https://github.com/ArvinZhuang/BiTAG
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/Asteur/Frame-semantic-SegRNN
Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold
https://github.com/BPMJG/annotated_pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Baidi96/text2sql
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/BillFoland/daisyluAMR
https://github.com/BorealisAI/DT-Fixup
https://github.com/BroCoLySTyLe/SQLovaReview
SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning
https://github.com/CX000/sqlnet_inference_py36
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/Cartus/DCGCN
Neural AMR: Sequence-to-Sequence Models for Parsing and Generation
https://github.com/CheesyB/cpointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ChernovAndrey/chronos-forecasting-wasserstein
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/ChunchuanLv/amr-evaluation-tool-enhanced
https://github.com/Cmeijiang/PointNet_MS
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/CurryTang/Graph-LLM
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?
https://github.com/DeepLearnXMU/CG-RL
StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing
https://github.com/Dir-b/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/DylanWusee/pointconv_pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ECE685-FinalProject/3D-object-recognition
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Fragjacker/Pointcloud-grad-CAM
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/GOD-GOD-Autonomous-Vehicle/self-pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Harut0726/my-pointnet-tensorflow
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/InsaneLife/ChineseNLPCorpus
https://github.com/JD-AI-Research-Silicon-Valley/auxiliary-task-for-text-to-sql
https://github.com/Jagyan/ghost_radarpoint_detection
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Javed002/Object_detection_PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/JiaPeng1234/MRI-Segmentation-Transformer
https://github.com/JunnYu/x-transformers-paddle
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/KAGUYAHONGLAI/SRC
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/KaidongLi/tf-3d-alpha
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/KhusDM/PointNetTree
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Ki6an/fastT5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/KiranAkadas/My_Pointnet_v2
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.