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/ppasupat/WikiTableQuestions
https://github.com/prasadsawant5/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/princeton-vl/SimpleView
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/pwc-1/Paper-10/tree/main/tapas
https://github.com/pwc-1/Paper-5/tree/main/mbart
https://github.com/pwc-1/Paper-9/tree/main/1/tapex
TAPEX: Table Pre-training via Learning a Neural SQL Executor
https://github.com/qipengguo/p2_webnlg2020
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/racheljose21/chatbot
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/realsonalkumar/Mish-Mash-Hackathon
https://github.com/rezkaaufar/fast-and-flexible
https://github.com/rijuldhir/TSM
https://github.com/ringgaard/sling
https://github.com/romaintha/pytorch_pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ronentk/dbca-splitter
Measuring Compositional Generalization: A Comprehensive Method on Realistic Data
https://github.com/ryanzhumich/sparc_atis_pytorch
https://github.com/s-nlp/russe_detox_2022
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/saaries/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/safakkbilici/Academic-Paper-Title-Recommendation
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/salokr/email-event-extraction
Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing
https://github.com/sanantoniochili/PointCloud_KNN
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/sarthakTUM/roofn3d
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/seominseok0429/inception-I3D-NON-LOCAL
https://github.com/servicenow/picard
PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models
https://github.com/shenminx/drs-parser
Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT
https://github.com/shishir-roy/decaNLP-with-BERT
The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/shivam017arora/Conversational-BI
https://github.com/shivamraval98/multitask-t5_ae
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/sigtyp/ST2024
The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/sinantie/NeuralAmr
Neural AMR: Sequence-to-Sequence Models for Parsing and Generation
https://github.com/skoltech-nlp/russe_detox_2022
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/smfirmin/DeepLearningFinalProject
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/soumik12345/point-cloud-segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/souvikshanku/translit-former
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/sunutf/TSM
https://github.com/swabhs/coling18tutorial
Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.