https://github.com/airc-keti/ke-t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness
Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
TreeGen: A Tree-Based Transformer Architecture for Code Generation
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
The Natural Language Decathlon: Multitask Learning as Question Answering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.