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Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge
[article]
2019
arXiv
pre-print
This paper provides an comprehensive analysis of the first shared task on End-to-End Natural Language Generation (NLG) and identifies avenues for future research based on the results. This shared task aimed 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. Introducing novel automatic and human metrics, we compare 62 systems submitted by 17
arXiv:1901.07931v2
fatcat:qlcgv2r66bfu3cp2foifzfaxbq