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

https://github.com/nithishkaviyan/Sentiment-Analysis-of-Yelp-Reviews

Convolutional Neural Networks for Sentence Classification

https://github.com/nithishkaviyan/Show-and-Tell-Neural-Network-Image-Caption-Generator-

Show and Tell: A Neural Image Caption Generator

https://github.com/niyunsheng/ems-sd

Factuality Enhanced Language Models for Open-Ended Text Generation

https://github.com/nkjsy/Neural-Machine-Translation

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

https://github.com/nku-hlt/diffeditor

CampNet: Context-Aware Mask Prediction for End-to-End Text-Based Speech Editing

https://github.com/nlp-research/bilm-tf

Evaluation of sentence embeddings in downstream and linguistic probing tasks

https://github.com/nlpat/MEGA-DT

Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis

https://github.com/nlpcl-lab/bert-event-extraction

Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation

https://github.com/nlpdata/dialogre

Dialogue-Based Relation Extraction

https://github.com/nlpdata/dream

DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension

https://github.com/nlx-group/overlapy

Language Models are Few-Shot Learners

https://github.com/no-execution/Summa_label

SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents

https://github.com/nocoolsandwich/pinyin_gen_error_sentence

Spelling Error Correction with Soft-Masked BERT

https://github.com/norahollenstein/reading-task-classification

Reading Task Classification Using EEG and Eye-Tracking Data

https://github.com/nouhadziri/THRED

Sequence to Sequence Learning with Neural Networks

https://github.com/nrc-cnrc/sockeye-multisource

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/ntunlp/eval-anaphora

Evaluating Pronominal Anaphora in Machine Translation: An Evaluation Measure and a Test Suite

https://github.com/ntunlp/ptrnet-depparser

Hierarchical Pointer Net Parsing

https://github.com/ntunlpsg/UnifiedParser_RST

A Unified Linear-Time Framework for Sentence-Level Discourse Parsing

https://github.com/nurpeiis/LeakGAN-PyTorch

Long Text Generation via Adversarial Training with Leaked Information

https://github.com/nvnhat95/Natural-Language-Inference

A Decomposable Attention Model for Natural Language Inference

https://github.com/nvshrao/AlignAndTranslate

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/nvshrao/Pytorch-GLUE

Convolutional Neural Networks for Sentence Classification

https://github.com/nyu-mll/CoLA-baselines

Neural Network Acceptability Judgments

https://github.com/nyu-mll/SentEval

SentEval: An Evaluation Toolkit for Universal Sentence Representations

https://github.com/nyu-mll/msgs

What do you learn from context? Probing for sentence structure in contextualized word representations

https://github.com/nyu-mll/multiNLI

A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

https://github.com/oarriaga/neural_image_captioning

Show and Tell: A Neural Image Caption Generator

https://github.com/obj2vec/obj2vec

CIRCE at SemEval-2020 Task 1: Ensembling Context-Free and Context-Dependent Word Representations

https://github.com/oddguan/audio_visual_video_caption

Sequence to Sequence -- Video to Text

https://github.com/off-clip/off-clip

OFF-CLIP: Improving Normal Detection Confidence in Radiology CLIP with Simple Off-Diagonal Term Auto-Adjustment

https://github.com/ohyj1002/persuasionforgood

Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good

https://github.com/olivia-fsm/p2mcq

Text Summarization with Pretrained Encoders

https://github.com/omidkashefi/contrapositive-inference

Toward Controlled Generation of Text

https://github.com/oneflow-inc/libai

SimCSE: Simple Contrastive Learning of Sentence Embeddings

https://github.com/onehaitao/CR-CNN-relation-extraction

Classifying Relations by Ranking with Convolutional Neural Networks

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