Papers with Code 아카이브

Sentence

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

0

평가표

0

보존된 평가 기록

0

코드

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

https://github.com/sai-prasanna/suggestion-mining-semeval19

Zoho at SemEval-2019 Task 9: Semi-supervised Domain Adaptation using Tri-training for Suggestion Mining

https://github.com/sai4july/custext

MedSTS: A Resource for Clinical Semantic Textual Similarity

https://github.com/saihiel/news_headlines

Educating Text Autoencoders: Latent Representation Guidance via Denoising

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

LipNet: End-to-End Sentence-level Lipreading

https://github.com/saisandeep97/Chat-botV2

Effective Approaches to Attention-based Neural Machine Translation

https://github.com/salaniz/pycocoevalcap

CIDEr: Consensus-based Image Description Evaluation

https://github.com/salanueva/UniVSE

UniVSE: Robust Visual Semantic Embeddings via Structured Semantic Representations

https://github.com/salesforce/auditnlg

SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization

https://github.com/salrowili/biom-transformers

Transferability of Natural Language Inference to Biomedical Question Answering

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

Language Models are Few-Shot Learners

https://github.com/sambit9238/deep_text_corrector

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/samim23/NeuralTalkAnimator

Show and Tell: A Neural Image Caption Generator

https://github.com/samnguyen8991/Facebook-MUSE

Unsupervised Machine Translation Using Monolingual Corpora Only

https://github.com/samrelins/tanda_search_qa_tool

TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection

https://github.com/saranya-venkatraman/gpt-who

ArguGPT: evaluating, understanding and identifying argumentative essays generated by GPT models

https://github.com/saraswat/munkhdalai-nse

Neural Semantic Encoders

https://github.com/sarveshsparab/BiLSTMCRFSeqTag

Bidirectional LSTM-CRF Models for Sequence Tagging

https://github.com/saubhik/chainer-image-captioning

Show and Tell: A Neural Image Caption Generator

https://github.com/saulhazelius/transformer-clustering

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

https://github.com/say2sarwar/DeepAcid

Effective Approaches to Attention-based Neural Machine Translation

https://github.com/sayhitosandy/Chatbot

Sequence to Sequence Learning with Neural Networks

https://github.com/sblayush/Tensorflow-Attention

Effective Approaches to Attention-based Neural Machine Translation

https://github.com/sblayush/summarization

Effective Approaches to Attention-based Neural Machine Translation

https://github.com/schwartznir/AbstrEncap

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/scofield7419/DiaRE-D2G

Dialogue-Based Relation Extraction

https://github.com/scrayish/ML_NLP

Language Models are Few-Shot Learners

https://github.com/sd2001/Auto-Image2Caption

Show and Tell: A Neural Image Caption Generator

https://github.com/sd2001/Image2Caption

Show and Tell: A Neural Image Caption Generator

https://github.com/seaweiqing/image2story

Neural Network Translation Models for Grammatical Error Correction

https://github.com/seaweiqing/neuraltalk_plus_charcnn

Neural Network Translation Models for Grammatical Error Correction

https://github.com/sebastianmacaluso/treenin

QCD-Aware Recursive Neural Networks for Jet Physics

https://github.com/sebiancoder/bertscorevisualizer

BERTScore: Evaluating Text Generation with BERT

https://github.com/securade/hub

Semantic-Conditional Diffusion Networks for Image Captioning

https://github.com/selmiss/gp-tlstgcn

Language Modeling with Gated Convolutional Networks

https://github.com/sen-pai/audio-word2vec-pytorch

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/seongminp/hdseg

Unsupervised Extractive Dialogue Summarization in Hyperdimensional Space

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