https://github.com/liuch37/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
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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
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
SeqGenSQL -- A Robust Sequence Generation Model for Structured Query Language
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
NormTab: Improving Symbolic Reasoning in LLMs Through Tabular Data Normalization
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
ValueNet: A Natural Language-to-SQL System that Learns from Database Information
StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
A Syntactic Neural Model for General-Purpose Code Generation
Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
A Syntactic Neural Model for General-Purpose Code Generation
TAGPRIME: A Unified Framework for Relational Structure Extraction
Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.