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

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

A Neural Attention Model for Abstractive Sentence Summarization

https://github.com/GauthierDmn/question_generation

Learning to Ask: Neural Question Generation for Reading Comprehension

https://github.com/GiuliaLanzillotta/exercises

Generating Sentences from a Continuous Space

https://github.com/GlassyWing/bi-lstm-crf

Bidirectional LSTM-CRF Models for Sequence Tagging

https://github.com/Gowthamkar6/Image-captioning-using-CNN-RNN

Show and Tell: A Neural Image Caption Generator

https://github.com/Guillem96/pointer-nn-pytorch

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/Guillem96/zsl-pytorch

Learning a Deep Embedding Model for Zero-Shot Learning

https://github.com/HHousen/TransformerSum

Text Summarization with Pretrained Encoders

https://github.com/HUSTLyn/SentEval

SentEval: An Evaluation Toolkit for Universal Sentence Representations

https://github.com/HaleyPei/Implement-of-SCA-CNN

SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning

https://github.com/HareeshBahuleyan/size-fit-net

Supervised Learning of Universal Sentence Representations from Natural Language Inference Data

https://github.com/HassanAzzam/Arabic-NER

Bidirectional LSTM-CRF Models for Sequence Tagging

https://github.com/HazyResearch/hgcn

Hyperbolic Neural Networks

https://github.com/HearyShen/HAN-SelfAttentive

A Structured Self-attentive Sentence Embedding

https://github.com/Helsinki-NLP/OpenNMT-py

Multilingual NMT with a language-independent attention bridge

https://github.com/Helsinki-NLP/shared-info

Phrase-Based & Neural Unsupervised Machine Translation

https://github.com/HemaDevaSagar35/NeuralMachineTranslation-French2English

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/HiJiGOO/self-attentive

A Structured Self-attentive Sentence Embedding

https://github.com/Houlong66/lattice_lstm_with_pytorch

Chinese NER Using Lattice LSTM

https://github.com/HsuWanTing/unified-summarization

A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss

https://github.com/Huawei-MRC-OSI/mrc-nlp-public

BERT for Joint Intent Classification and Slot Filling

https://github.com/HughKu/Im2txt

Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge

https://github.com/HuihuiChyan/BJTUNLP_Practice2020

Convolutional Neural Networks for Sentence Classification

https://github.com/HuihuiChyan/BJTUNLP_Practice2021

Convolutional Neural Networks for Sentence Classification

https://github.com/ICSEG/M2TS

Recommendations for Datasets for Source Code Summarization

https://github.com/INK-USC/AFET

Context-Dependent Fine-Grained Entity Type Tagging

https://github.com/INK-USC/CommonGen

CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning

https://github.com/INK-USC/REGD

NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction

https://github.com/INK-USC/procedural-extraction

Eliciting Knowledge from Experts:Automatic Transcript Parsing for Cognitive Task Analysis

https://github.com/IS5882/Open-CyKG

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/IgnacioHeredia/plant_classification

Top-down Visual Saliency Guided by Captions

https://github.com/IljaSamoilov/subtitle_prettifier

Calculating the similarity between words and sentences using a lexical database and corpus statistics

https://github.com/ImperialNLP/BertGen

VL-BERT: Pre-training of Generic Visual-Linguistic Representations

https://github.com/IndicoDataSolutions/finetune

Convolutional Neural Networks for Sentence Classification

https://github.com/InnerPeace-Wu/im2p-tensorflow

A Hierarchical Approach for Generating Descriptive Image Paragraphs

https://github.com/InsaneLife/dssm

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

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