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Text Generation
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>
하위 태스크
DG
Data-to-Text Generation
CD
Code Documentation Generation
DG
Dialogue Generation
TG
Table-to-Text Generation
TS
Text Style Transfer
MS
Multi-Document Summarization
FT
Fact-based Text Editing
PG
Paraphrase Generation
SG
Story Generation
CT
Conditional Text Generation
SC
Spelling Correction
HG
Headline Generation
PG
Paper generation
PG
Profile Generation
CG
Concept-To-Text Generation
DG
Distractor Generation
NL
Natural Language Landmark Navigation Instructions Generation
HG
Hint Generation
MD
Molecular description generation
NG
News Generation
Q
Question-Answer-Generation
RG
Rules-of-thumb Generation
SG
Sonnet Generation
SC
Story Completion
TI
Text Infilling