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

https://github.com/yinghao1019/Joint_learn

BERT for Joint Intent Classification and Slot Filling

https://github.com/yinghao1019/NLP_and_DL_practice

Convolutional Neural Networks for Sentence Classification

https://github.com/yinghao1019/NLP_and_DL_practice/blob/master/Convolution_Neural_Netowrks_for_sentence_classification_Practice.ipynb

Convolutional Neural Networks for Sentence Classification

https://github.com/yinghao1019/imdb_prac

Combining Similarity Features and Deep Representation Learning for Stance Detection in the Context of Checking Fake News

https://github.com/yipingnus/scratchplot-story-generation

Generating Datasets with Pretrained Language Models

https://github.com/yiwuzhong/sub-gc

Comprehensive Image Captioning via Scene Graph Decomposition

https://github.com/yjiangcm/dcpcse

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

https://github.com/ykrmm/ICLR_2020

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/ykrmm/TREMBA

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/yoavg/bert-syntax

Targeted Syntactic Evaluation of Language Models

https://github.com/yongjincho/cnn-text-classification-pytorch

Convolutional Neural Networks for Sentence Classification

https://github.com/yonguno/cs410explain

Learning to Explain Non-Standard English Words and Phrases

https://github.com/yoonkim/CNN_sentence

Convolutional Neural Networks for Sentence Classification

https://github.com/yoonkim/neural-qcfg

StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer

https://github.com/youichiro/transformer-copy

Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data

https://github.com/young-zonglin/bilm-tf-extended

Evaluation of sentence embeddings in downstream and linguistic probing tasks

https://github.com/yschoi-nisp/AI-Grand-Challenge-2020

Convolutional Neural Networks for Sentence Classification

https://github.com/yscope75/CS2225.CH2001020

Sequence to Sequence Learning with Neural Networks

https://github.com/yuantiku/fairseq-gec

Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data

https://github.com/yuanyu255/PCNN_C2SA

Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction

https://github.com/yueyongjiao/Transformer-Contextual

A Structured Self-attentive Sentence Embedding

https://github.com/yueyongjiao/attention_is_all_you_need_pytorch

A Structured Self-attentive Sentence Embedding

https://github.com/yuhaonankaka/adl_maskrcnn

ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language

https://github.com/yuhui-zh15/nlg_metrics

BERTScore: Evaluating Text Generation with BERT

https://github.com/yukio326/gan-nmt

Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets

https://github.com/yur7nd/ptss

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

https://github.com/yurayli/image-caption-pytorch

Show and Tell: A Neural Image Caption Generator

https://github.com/yurayli/image_caption_pytorch

Show and Tell: A Neural Image Caption Generator

https://github.com/yurayli/stanford-cs224n-sol

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/yuriak/PPDAI

A Structured Self-attentive Sentence Embedding

https://github.com/yuweisunn/homogeneous-learning

The Consciousness Prior

https://github.com/yuxiaw/openfactcheck

Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers

https://github.com/yuyangdu01/llm-cl-vqa

Surgical-VQA: Visual Question Answering in Surgical Scenes using Transformer

https://github.com/yuyay/chainer_nic

Show and Tell: A Neural Image Caption Generator

https://github.com/ywu94/Code-Notes

Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence

https://github.com/yya518/FinBERT

Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts

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