In E-commerce, a key challenge in text generation is to find a good trade-off between word diversity and accuracy (relevance) in order to make generated text appear more natural and human-like. In order to improve the relevance of generated results, conditional text generators were developed that use input keywords or attributes to produce the corresponding text. Prior work, however, do not finely control the diversity of automatically generated sentences. For example, it does not control the order of keywords to put more relevant ones first. Moreover, it does not explicitly control the balance between diversity and accuracy. To remedy these problems, we propose a fine-grained controllable generative model, called~textit{Apex}, that uses an algorithm borrowed from automatic control (namely, a variant of the textit{proportional, integral, and derivative (PID) controller}) to precisely manipulate the diversity/accuracy trade-off of generated text. The algorithm is injected into a Conditional Variational Autoencoder (CVAE), allowing textit{Apex} to control both (i) the order of keywords in the generated sentences (conditioned on the input keywords and their order), and (ii) the trade-off between diversity and accuracy. Evaluation results on real world datasets~footnote{The sampled data is publicly available at url{}} show that the proposed method outperforms existing generative models in terms of diversity and relevance. Moreover, it achieves about 97% accuracy in the control of the order of keywords. textit{Apex} is currently deployed to generate production descriptions and item recommendation reasons in Taobaofootnote{}, the largest E-commerce platform in China. The A/B production test results show that our method improves click-through rate (CTR) by 13.17% compared to the existing method for production descriptions. For item recommendation reason, it is able to increase CTR by 6.89% and 1.42% compared to user reviews and top-K item recommendation without reviews, respectively.

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