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/borisdayma/clip-jax
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/bplank/teaching-dl4nlp
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/bpucla/latent-space-EBM-prior
https://github.com/brainsqueeze/text2vec
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/brandontrabucco/scaled_dot_product_attention
https://github.com/brightmart/bert_customized
https://github.com/brightmart/bert_original
https://github.com/brightmart/text_classification
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/brown-palm/ObjectPrompt
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/brsarah20/Alphafold2
https://github.com/bruthyu/bpt-vlm
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/brynnchernosky/seq2seq
Keeping Notes: Conditional Natural Language Generation with a Scratchpad Mechanism
https://github.com/bshantam97/Attention_Based_Networks
https://github.com/btrnt/butternut
GLTR: Statistical Detection and Visualization of Generated Text
https://github.com/bupt-ai-cz/Meta-SelfLearning
Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition
https://github.com/buseskorkmaz/fms-at-work
Offline RL for Natural Language Generation with Implicit Language Q Learning
https://github.com/butsugiri/shape
https://github.com/buyeah1109/KEN
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/buyeah1109/finc
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/bwallace/RCT-summarization-data
https://github.com/bytedance/neurst
https://github.com/bywords/lang-style-transfer-legacy
https://github.com/bzhangGo/zero
https://github.com/bzylich/persona-chatbot
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/c00k1ez/plain-transformers
https://github.com/cadia-lvl/fastspeech2
https://github.com/cahya-wirawan/indonesian-language-models
https://github.com/caiy0220/xproax
Educating Text Autoencoders: Latent Representation Guidance via Denoising
https://github.com/cambridgeltl/pairs
Fairer Preferences Elicit Improved Human-Aligned Large Language Model Judgments
https://github.com/camlaedtke/NLP
https://github.com/canneltigrou/testHandwritting
https://github.com/caoyujiALgLM/NLP
https://github.com/caoyujiALgLM/NLP-progress
https://github.com/cas995/Thesis-PDG
Towards Knowledge-Based Personalized Product Description Generation in E-commerce
https://github.com/catwang42/Covid_bert_machine_comprehension
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.