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 ChallengePaper | Code | Results | Date | Stars |
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