Routing Enforced Generative Model for Recipe Generation. - Yu, Zhiwei; Zang, Hongyu; Wan, Xiaojun (Empirical Methods in Natural Language Processing, 2020)
One of the most challenging part of recipe generation is to deal with the complex restrictions among the input ingredients. Previous researches simplify the problem by treating the inputs independently and generating recipes containing as much information as possible. In this work, we propose a routing method to dive into the content selection under the internal restrictions. The routing enforced generative model (RGM) can generate appropriate recipes according to the given ingredients and user preferences. Our model yields new state-of-the-art results on the recipe generation task with significant improvements on BLEU, F1 and human evaluation.