  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 show that the proposed method
outperforms existing generative models in terms of diversity and relevance.
Apex is currently deployed to generate production descriptions and item
recommendation reasons in Taobao owned by Alibaba, 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.
