Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge (2019)
Abstract
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Bibliographic Information
Digital Object Identifier: http://dx.doi.org/10.48550/arxiv.1901.07931
Publication URI: https://arxiv.org/abs/1901.07931
Type: Other