https://github.com/swabhs/open-sesame
Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold
Papers with Code 아카이브
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/tanmay1618/text_to_sql_bert
A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization
https://github.com/taoyds/grappa
GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing
https://github.com/taoyds/sparc
https://github.com/tdozat/Parser-v3
https://github.com/tea1528/Non-Local-NN-Pytorch
https://github.com/tensorflow/models/tree/master/research/seq_flow_lite
https://github.com/theSparta/neural-symbolic-machines
Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
https://github.com/thecodemasterk/Text-to-Text-transfer-transformers
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/thousfeet/lear
Learning Algebraic Recombination for Compositional Generalization
https://github.com/thu-keg/omnievent
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/thudm/swissarmytransformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/tidbcloud/chat2query_bench
Towards Automated Cross-domain Exploratory Data Analysis through Large Language Models
https://github.com/timothylimyl/PointNet-Pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/tinymilky/RSANet
https://github.com/tiwarikajal/Seq2SQL-
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/tomerwolgithub/Break
https://github.com/tonysy/PyAction
https://github.com/tyjiangu/fido
Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold
https://github.com/um-arm-lab/efficient-eng-2-ltl
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/unbiarirang/fixed-input-parameterization
https://github.com/vanoracai/Exploiting-Spatial-temporal-Relationships-for-3D-Pose-Estimation-via-Graph-Convolutional-Networks
https://github.com/vgaraujov/seq2seq-spanish-plms
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/vinits5/learning3d
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/volcengine/vegiantmodel
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/vzhong/gazp
Grounded Adaptation for Zero-shot Executable Semantic Parsing
https://github.com/wangcongcong123/ttt
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/wangxinyu0922/Second_Order_Parsing
Second-Order Semantic Dependency Parsing with End-to-End Neural Networks
https://github.com/witignite/Frustum-PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/wonderland-dsg/pointnet-grid
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/wronnyhuang/SQLNet_inference
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/wuryantoAji/POINTNET
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/xgenerationlab/xiyan_mcp_server
A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL
https://github.com/xiaojunxu/SQLNet
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/xinyu1205/recognize-anything
https://github.com/xiuyu0000/new_papers_codes/tree/main/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.