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/coronazap/bert_client
https://github.com/cosmoquester/2021-dialogue-summary-competition
https://github.com/cosmoquester/seq2seq
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/cosmoquester/transformers-bart-pretrain
https://github.com/cospplay/bert-master
https://github.com/cove-adml/adml-anon
https://github.com/coxy1989/tfmr
https://github.com/cpm0722/transformer_pytorch
https://github.com/cpmpercussion/keras-mdn-layer
https://github.com/csong27/auditing-text-generation
https://github.com/cuber2460/bert
https://github.com/cui0523/Code6/tree/main/duconv
Multiple Generative Models Ensemble for Knowledge-Driven Proactive Human-Computer Dialogue Agent
https://github.com/cui0523/Code6/tree/main/mass
MASS: Masked Sequence to Sequence Pre-training for Language Generation
https://github.com/cyberzhg/keras-pos-embd
https://github.com/czero69/acomoeye-NN
https://github.com/d-ailin/blind-faith-in-text
https://github.com/d-gurgurov/im2latex
TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
https://github.com/d223302/merging-facts-crafting-fallacies
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
https://github.com/da03/Residual-EBM
https://github.com/daQuincy/DeepMusicvStyle
mu-Forcing: Training Variational Recurrent Autoencoders for Text Generation
https://github.com/dalek7/attention
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/dalmia/Quora-Question-Pairs
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/damo-nlp-sg/videollama2
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
https://github.com/damo-nlp-sg/videollama3
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
https://github.com/danielajisafe/Image-Captioning-Model
https://github.com/danieldeutsch/bert_score_content_analysis
https://github.com/dannigt/fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/dannigt/kit-iwslt2023-multilingual
https://github.com/danny911kr/REALTALK
Evaluating Very Long-Term Conversational Memory of LLM Agents
https://github.com/daphne12345/SummarizationRadiologyReports
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/daqingliu/CAVP
Context-Aware Visual Policy Network for Fine-Grained Image Captioning
https://github.com/datalogue/keras-attention
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/davidalbertonogueira/NLP-tutorials
https://github.com/davidgolub/QuestionGeneration
https://github.com/davidgolub/charizard
Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.