https://github.com/zhanzecheng/IRNet-1
공식 코드Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation
Papers with Code 아카이브
Semantic Parsing
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 7
- 평가표
- 20
- 보존된 평가 기록
- 78
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/zhshlii/petsql
공식 코드PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency
https://github.com/zhuang-li/Contextual-Semantic-Parsing-Paper-List
공식 코드https://github.com/zhuang-li/cl_nsp
공식 코드Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers
https://github.com/zhuang-li/few-shot-semantic-parsing
공식 코드https://github.com/zhxlia/murre
공식 코드MURRE: Multi-Hop Table Retrieval with Removal for Open-Domain Text-to-SQL
https://github.com/zirui-HIT/DAC
공식 코드https://github.com/zirui-HIT/Fused
공식 코드Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQL
https://github.com/ziwang-klvk/cfq-rbmt
공식 코드On Evaluating Multilingual Compositional Generalization with Translated Datasets
https://github.com/zliucr/X2Parser
공식 코드X2Parser: Cross-Lingual and Cross-Domain Framework for Task-Oriented Compositional Semantic Parsing
https://github.com/zysszy/GrammarCNN
공식 코드https://github.com/zysszy/TreeGen
공식 코드TreeGen: A Tree-Based Transformer Architecture for Code Generation
https://worksheets.codalab.org/worksheets/0x4d6dbfc5ec7f44a6a4da4ca2a9334d6e
공식 코드Macro Grammars and Holistic Triggering for Efficient Semantic Parsing
https://worksheets.codalab.org/worksheets/0x50757a37779b485f89012e4ba03b6f4f
공식 코드https://worksheets.codalab.org/worksheets/0x593676a278fc4e5abe2d8bac1e3df486
공식 코드Unanimous Prediction for 100% Precision with Application to Learning Semantic Mappings
https://worksheets.codalab.org/worksheets/0x88c914ee1d4b4a4587a07f36f090f3e5
공식 코드From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood
https://worksheets.codalab.org/worksheets/0x91d77db37e0a4bbbaeb37b8972f4784f
공식 코드Evaluating Semantic Parsing against a Simple Web-based Question Answering Model
https://worksheets.codalab.org/worksheets/0x9fe4d080bac944e9a6bd58478cb05e5e
공식 코드https://worksheets.codalab.org/worksheets/0xad3fc9f52f514e849b282a105b1e3f02
공식 코드Simpler Context-Dependent Logical Forms via Model Projections
https://worksheets.codalab.org/worksheets/0xdec998f58deb4829aba80fbf49f69236
공식 코드https://worksheets.codalab.org/worksheets/0xf26cd79d4d734287868923ad1067cf4c
공식 코드https://gitee.com/gai-shaoyan/mind3d
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2023-MindSpore-1/ms-code-164
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/2023-MindSpore-1/ms-code-35
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2023-MindSpore-4/Code10/tree/main/frustum-pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/2023-MindSpore-4/Code9/tree/main/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/esm
ESM+: Modern Insights into Perspective on Text-to-SQL Evaluation in the Age of Large Language Models
https://github.com/2024-MindSpore-1/Code4/tree/main/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2024-MindSpore-1/Code7/tree/main/crnn_seq2seq_ocr
https://github.com/AI-Guru/pointcloud_experiments
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Alex-Fabbri/lang2logic-PyTorch
https://github.com/AlfredoZermini/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Alibaba-NLP/MultilangStructureKD
Second-Order Semantic Dependency Parsing with End-to-End Neural Networks
https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/graphix
Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.