https://github.com/mgaido91/FBK-fairseq-ST
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
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.
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
MASS: Masked Sequence to Sequence Pre-training for Language Generation
Learning Transferable Visual Models From Natural Language Supervision
M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-training
MASS: Masked Sequence to Sequence Pre-training for Language Generation
BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining
Structured Denoising Diffusion Models in Discrete State-Spaces
Get my drift? Catching LLM Task Drift with Activation Deltas
Neural Machine Translation by Jointly Learning to Align and Translate
MASS: Masked Sequence to Sequence Pre-training for Language Generation
Neural Machine Translation by Jointly Learning to Align and Translate
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Learning Transferable Visual Models From Natural Language Supervision
Learning Transferable Visual Models From Natural Language Supervision
MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.