E2E NLG Challenge: Neural Models vs. Templates

WS 2018  ·  Yevgeniy Puzikov, Iryna Gurevych ·

E2E NLG Challenge is a shared task on generating restaurant descriptions from sets of key-value pairs. This paper describes the results of our participation in the challenge. We develop a simple, yet effective neural encoder-decoder model which produces fluent restaurant descriptions and outperforms a strong baseline. We further analyze the data provided by the organizers and conclude that the task can also be approached with a template-based model developed in just a few hours.

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Data-to-Text Generation E2E NLG Challenge TUDA BLEU 56.57 # 9
NIST 7.4544 # 9
METEOR 45.29 # 1
ROUGE-L 66.14 # 9
CIDEr 1.8206 # 8


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