https://github.com/realmichaelye/NLP-State-of-the-art-Style-Transfer
Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer
Papers with Code 아카이브
Text Generation
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- 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/rebnej/lick-caption-bias
https://github.com/redcaps-dataset/redcaps-downloader
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/redevaaa/Transformer-for-EEG
https://github.com/reichang182/variable-length-piano-infilling
https://github.com/remilb/ghissu-bot
A Dual Encoder Sequence to Sequence Model for Open-Domain Dialogue Modeling
https://github.com/reml-group/dog
ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
https://github.com/reppy4620/Dialog
https://github.com/ricardordb/bert
https://github.com/richard-peng-xia/Chinese-Noisy-Text
https://github.com/richard88888/chinese-noisy-text
https://github.com/richinkabra/CoVe-BCN
https://github.com/rickltt/event_detection
DEGREE: A Data-Efficient Generation-Based Event Extraction Model
https://github.com/rickyHong/Google-BERT-repl
https://github.com/ricsinaruto/NeuralChatbots-DataFiltering
https://github.com/ricsinaruto/dialog-eval
https://github.com/rileynwong/pytorch-seq2seq-joke2punchline
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/rinnakk/japanese-clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/rishavbb/Image_Captioning
https://github.com/rishavbhurtel/sentiment-classifier
https://github.com/riversdie/chatbot
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/rj-IIITH18/style_transfer_in_text
https://github.com/rmokady/clip_prefix_caption
Unified Vision-Language Pre-Training for Image Captioning and VQA
https://github.com/robertknight/textgen
https://github.com/roberttwomey/machine-imagination-isea
On the Multilingual Capabilities of Very Large-Scale English Language Models
https://github.com/robinsongh381/unilm_pytorch_korean
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/robotics-4-all/ISSEL-Announcements
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/rockyyliang/handwrite
https://github.com/rohan400/PepperQA
https://github.com/rojagtap/abstractive_summarizer
https://github.com/romankoshkin/transllama
https://github.com/ronakdm/irl-text-generation
Toward Diverse Text Generation with Inverse Reinforcement Learning
https://github.com/rongjiehuang/transpeech
https://github.com/rpryzant/delete_retrieve_generate
Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer
https://github.com/rpuiggari/bert2
https://github.com/ruanchaves/eplm
TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.