https://github.com/m-hahn/PMLM
Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order
Shall I Compare Thee to a Machine-Written Sonnet? An Approach to Algorithmic Sonnet Generation
Learning Transferable Visual Models From Natural Language Supervision
Learning Transferable Visual Models From Natural Language Supervision
Learning Transferable Visual Models From Natural Language Supervision
Quantitative Argument Summarization and Beyond: Cross-Domain Key Point Analysis
Wizard of Wikipedia: Knowledge-Powered Conversational agents
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation
Neural Machine Translation by Jointly Learning to Align and Translate
FaithDial: A Faithful Benchmark for Information-Seeking Dialogue
Unsupervised Natural Language Generation with Denoising Autoencoders
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
Improving Language Generation with Sentence Coherence Objective
Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration
Learning Transferable Visual Models From Natural Language Supervision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.