Text Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 25
- 평가표
- 155
- 보존된 평가 기록
- 54
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/ChristophReich1996/Mode_Collapse
https://github.com/Colanim/BERT_STS-B
https://github.com/CongBao/ChatBot
https://github.com/DCYN/Ramdomized-Clinical-Trail-Classification
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/DJJune/Transformer
https://github.com/DKhomi/PrefixTuning
https://github.com/DM2-ND/InjType
https://github.com/DabiriAghdam/SimMark
SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation
https://github.com/Danial-Alh/fast-bleu
Jointly Measuring Diversity and Quality in Text Generation Models
https://github.com/DanielaStep/SC
Scoring Sentence Singletons and Pairs for Abstractive Summarization
https://github.com/DanielaStep/sentence_similarity_rouge
Scoring Sentence Singletons and Pairs for Abstractive Summarization
https://github.com/DanieleVeri/deep_comedy
https://github.com/Data-drone/cvnd_image_captioning
https://github.com/DataScienceNigeria/Giant-Language-Model-Test-Room-GLTR-
GLTR: Statistical Detection and Visualization of Generated Text
https://github.com/DataScienceNigeria/WriterForcing-by-Carnegie-Mellon-University
https://github.com/Dave870907/Transformer
https://github.com/DavidHarar/Siamese-Networks-for-name-nickname-similarity
https://github.com/Deeksha96/Im2Latex
https://github.com/DeligientSloth/QQsim
https://github.com/DeligientSloth/bert-tensorflow
https://github.com/DeokO/bert-excercise-ongoing
https://github.com/DevHyung/dialogue_cl
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/DevSinghSachan/multilingual_nmt
https://github.com/DimasDMM/nlp-completer
https://github.com/DimasDMM/transformers
https://github.com/Djmcflush/Quantum-Hackathon
https://github.com/Doffery/BERT-Sentiment-Analysis-Amazon-Review
https://github.com/DorinK/Assignment-2-Neural-Machine-Translation
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/EVOKE-LMM/EVOKE
In-Context Editing: Learning Knowledge from Self-Induced Distributions
https://github.com/EagleW/Writing-editing-Network
https://github.com/EdinburghNLP/XSum
https://github.com/EmilioTyl/transformers_from_scratch
https://github.com/Enigmatisms/Set-Transformer
https://github.com/Epoch-Mengying/Generating-Poetry-with-Chatbot
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/ErikEkstedt/TurnGPT
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.