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/vinodkkurmi/PQG
Learning Semantic Sentence Embeddings using Sequential Pair-wise Discriminator
https://github.com/virginie-do/e-SNLI-VE
e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks
https://github.com/vis-nlp/chart-to-text
Chart-to-Text: A Large-Scale Benchmark for Chart Summarization
https://github.com/vishnuanil122/Bert-Binary-Text-Classifier
https://github.com/vladbataev/handwriting_synthesis
https://github.com/vpj/jax_transformer
https://github.com/vulus98/rethinking-attention
https://github.com/vvvm23/TchAIkovsky-Legacy
https://github.com/vvvm23/sundae
https://github.com/walkacross/transformer-pytorch
https://github.com/wangmz15/Chinese-Error-Correction-with-THUMT
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/wangxfng/rdrec
Enhancing High-order Interaction Awareness in LLM-based Recommender Model
https://github.com/wayalhruhi/gogle_bert
https://github.com/wayalhruhi/julianser
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
https://github.com/wchh127/yykf
https://github.com/wei-ln/versatile-graph-learning-approaches
https://github.com/weilinie/RelGAN
https://github.com/weipingfu/qgeval
https://github.com/wesselreijngoud/masterthesis2019
MoNoise: Modeling Noise Using a Modular Normalization System
https://github.com/whr94621/NJUNMT-pytorch
https://github.com/why2000/DuReader-bert
https://github.com/willspag/SeqGan
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
https://github.com/willxxy/ecg-bench
ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling
https://github.com/windweller/PragmaticVQA
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/wl-research/nubia
NUBIA: NeUral Based Interchangeability Assessor for Text Generation
https://github.com/wtliao/layout2img
https://github.com/wxt1997/Transformer-Transducer
https://github.com/wyu97/Easy-use-BART
https://github.com/wyu97/permgen
https://github.com/wzlxjtu/PositionalEncoding2D
https://github.com/xbtlin/All-about-Machine-Learning
https://github.com/xcmyz/FastSpeech
https://github.com/xesdiny/test-bert-master
https://github.com/xhlulu/arxiv-assistant
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/xiaoiker/meta_dpm
Structured Denoising Diffusion Models in Discrete State-Spaces
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.