  This work addresses an alternative approach for query expansion (QE) using a
generative adversarial network (GAN) to enhance the effectiveness of
information search in e-commerce. We propose a modified QE conditional GAN
(mQE-CGAN) framework, which resolves keywords by expanding the query with a
synthetically generated query that proposes semantic information from text
input. We train a sequence-to-sequence transformer model as the generator to
produce keywords and use a recurrent neural network model as the discriminator
to classify an adversarial output with the generator. With the modified CGAN
framework, various forms of semantic insights gathered from the query document
corpus are introduced to the generation process. We leverage these insights as
conditions for the generator model and discuss their effectiveness for the
query expansion task. Our experiments demonstrate that the utilization of
condition structures within the mQE-CGAN framework can increase the semantic
similarity between generated sequences and reference documents up to nearly 10%
compared to baseline models
