E2E
End-to-End NLG Challenge
홈페이지 · 논문 90편
End-to-End NLG Challenge (E2E) aims to assess whether recent end-to-end NLG systems can generate more complex output by learning from datasets containing higher lexical richness, syntactic complexity and diverse discourse phenomena. Source: [Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge](/paper/evaluating-the-state-of-the-art-of-end-to-end)
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