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/gsatallion8/Framenet-Frame-Parser
Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold
https://github.com/gsh2014/grammar-coarse2fine
A Syntactic Neural Model for General-Purpose Code Generation
https://github.com/gulucaptain/dynamictrl
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/guotong1988/weak-supervised-Rule-Text2SQL
Using Database Rule for Weak Supervised Text-to-SQL Generation
https://github.com/halecakir/Sent2LogicalForm
https://github.com/happen2me/kqa-pro-datasets
https://github.com/heyanger/sqltools
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task
https://github.com/hnVfly/pointnet.mxnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/hnmspirit/pointnet1
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/huggingface/transformers
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/hugochan/PFoodReq
Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases
https://github.com/huyz1117/Non_Local_Net_TensorFlow
https://github.com/hz-ants/pointnet.pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/imanlab/deep_movement_primitives
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/inyukwo1/Coarse2fine_boilerplate
https://github.com/ist-daslab/rosa
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/itzprashu1/prashant
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/jiajunhua/facebookresearch-Detectron
https://github.com/jongwooko/nash-pruning-official
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/jordiae/DeepLearning-MAI
https://github.com/josefigueroa168/NLP-project
https://github.com/jpviguerasguillen/feedback-non-local-attention-fnla
https://github.com/jpviguerasguillen/feedback-non-local-attention_fNLA
https://github.com/jtpils/treegan
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/junnyu/paddle_t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/kamalkraj/TAPAS-TF2
https://github.com/kasnerz/tabgenie
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/kh-mo/QA_wikisql
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/khakhazeus/bibl
https://github.com/kitnil/awesome
NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System
https://github.com/kzCassie/ucl_nlp
StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing
https://github.com/lancelot1998/aigclifecyclemanagement
https://github.com/lesterpjy/numeric-t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/lingzhang1/pointnet_pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/lingzhang1/pointnet_tensorflow
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.