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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/DataZwer/CNNTextClassification

Convolutional Neural Networks for Sentence Classification

https://github.com/DavidMortensen/Bachelor-readinglist

Relation Classification via Recurrent Neural Network

https://github.com/DeNeutoy/Decomposable_Attn

A Decomposable Attention Model for Natural Language Inference

https://github.com/DeepGraphLearning/FewShotRE

Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs

https://github.com/Demfier/ebm-sanskrit-word-segmentation

Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit

https://github.com/DimasDMM/diseases-ner

Bidirectional LSTM-CRF Models for Sequence Tagging

https://github.com/Djmcflush/Quantum-Hackathon

Show and Tell: A Neural Image Caption Generator

https://github.com/Domanjiri/joint-bert-with-tf2

BERT for Joint Intent Classification and Slot Filling

https://github.com/DongjunLee/text-cnn-tensorflow

Convolutional Neural Networks for Sentence Classification

https://github.com/DorinK/Assignment-2-Neural-Machine-Translation

Effective Approaches to Attention-based Neural Machine Translation

https://github.com/DorinK/Implementing-an-SNLI-Paper

Shortcut-Stacked Sentence Encoders for Multi-Domain Inference

https://github.com/DsSaurabh/SkimLit-Project

Neural Networks for Joint Sentence Classification in Medical Paper Abstracts

https://github.com/ESBigeard/paper_graph

BioSentVec: creating sentence embeddings for biomedical texts

https://github.com/EdinburghNLP/XSum

Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization

https://github.com/EightRice/atn_GPT-3

Language Models are Few-Shot Learners

https://github.com/EleutherAI/gpt-neo

Language Models are Few-Shot Learners

https://github.com/EleutherAI/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/EleutherAI/lm_evaluation_harness

Language Models are Few-Shot Learners

https://github.com/Elvirasun28/quora-question-duplicate

Bilateral Multi-Perspective Matching for Natural Language Sentences

https://github.com/EmpathYang/ADEPT

Debiasing Pre-trained Contextualised Embeddings

https://github.com/EngSalem/ArabicTextClassification

A C-LSTM Neural Network for Text Classification

https://github.com/EngSalem/Text-Classification-of-the-shelf-with-a-normalizer-for-Arabic-text-

A C-LSTM Neural Network for Text Classification

https://github.com/EngSalem/TextClassification_Off_the_shelf

A C-LSTM Neural Network for Text Classification

https://github.com/Epoch-Mengying/Generating-Poetry-with-Chatbot

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/Erutan-pku/LCN-for-Chinese-QA

Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese Pre-trained Language Models

https://github.com/EugeneSel/EUMT

WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia

https://github.com/EuphoriaYan/ChatRobot-For-Keras2

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/ExplorerFreda/Structured-Self-Attentive-Sentence-Embedding

A Structured Self-attentive Sentence Embedding

https://github.com/FastAndFourier/MLA-Project-DYSARTHRIA

Learning to detect dysarthria from raw speech

https://github.com/Fengdalu/LipNet-PyTorch

LipNet: End-to-End Sentence-level Lipreading

https://github.com/Flo-tyna/Biomedical-Text-Summarizer

Text Summarization with Pretrained Encoders

https://github.com/FreddeFrallan/Contrastive-Tension

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

https://github.com/FreddeFrallan/Multilingual-CLIP

Language-agnostic BERT Sentence Embedding

https://github.com/GT-SALT/AAAI_CLF

Semi-Supervised Models via Data Augmentationfor Classifying Interactive Affective Responses

https://github.com/GallupGovt/multivac

Domain Adapted Word Embeddings for Improved Sentiment Classification

https://github.com/Ganeshpadmanaban/Neural-Attention-Model

A Neural Attention Model for Abstractive Sentence Summarization

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.