Papers with Code 아카이브
Text Generation
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Text Generation is the task of generating text with the goal of appearing indistinguishable to human-written text. This task is more formally known as "natural language generation" in the literature.
Text generation can be addressed with Markov processes or deep generative models like LSTMs. Recently, some of the most advanced methods for text generation include BART, GPT and other GAN-based approaches. Text generation systems are evaluated either through human ratings or automatic evaluation metrics like METEOR, ROUGE, and BLEU.
Further readings:
<span style="color:grey; opacity: 0.6">( Image credit: Adversarial Ranking for Language Generation )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/songmzhang/dskd
https://github.com/songys/Kosentiment
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
https://github.com/songys/KosentimentalTreebank
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
https://github.com/sonrisa07/transformer
https://github.com/sooftware/Attention-Implementation
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/sooftware/attentions
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/sooftware/nlp-attentions
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/sotelo/scribe
https://github.com/soumik12345/Automatic-Number-Plate-Recognition
https://github.com/soumik12345/transformer.pytorch
https://github.com/souvikshanku/digit-captioning
https://github.com/souvikshanku/translit-former
https://github.com/sovit-123/Deep-Learning-Image-Captioning
https://github.com/spokoyny/spokoyny.github.io
Lagging Inference Networks and Posterior Collapse in Variational Autoencoders
https://github.com/srgrace/contextual-compression
Extending Context Window of Large Language Models via Semantic Compression
https://github.com/srikanth-sfu/lyrebird
https://github.com/srir-crypto/fake_news
https://github.com/stair-lab/villm-eval
https://github.com/stanfordnlp/axbench
https://github.com/stanfordnlp/dsp
https://github.com/stefannatu/NLP-Translation-Models
https://github.com/stevezheng23/fairseq_extension
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/stevinc/Transformer_Timeseries
https://github.com/stingram/CV_Image_Captioning
https://github.com/studian/CVND_P2_Image_Captioning
https://github.com/studio-ousia/luke
https://github.com/stupidbuluchacha/urbanclip
UrbanVLP: Multi-Granularity Vision-Language Pretraining for Urban Socioeconomic Indicator Prediction
https://github.com/sueqian6/ACL2019-Reducing-Gender-Bias-in-Word-Level-Language-Models-Using-A-Gender-Equalizing-Loss-Function
Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Function
https://github.com/sueqian6/Reducing-Gender-Bias-in-Word-Level-Language-Models-Using-A-Gender-Equalizing-Loss-Function
Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Function
https://github.com/suhoy901/SeqGAN
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
https://github.com/sumilergao/sungen
ZeroGen: Efficient Zero-shot Learning via Dataset Generation
https://github.com/sunnysinghnitb/text-corrector-software
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/sunnysinghnitb/text_corrector_software
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/sunsmarterjie/chatterbox
https://github.com/sunzeyeah/rlhf
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.