  A well-known way to improve the performance of document retrieval is to
expand the user's query. Several approaches have been proposed in the
literature, and some of them are considered as yielding state-of-the-art
results in IR. In this paper, we explore the use of text generation to
automatically expand the queries. We rely on a well-known neural generative
model, GPT-2, that comes with pre-trained models for English but can also be
fine-tuned on specific corpora. Through different experiments, we show that
text generation is a very effective way to improve the performance of an IR
system, with a large margin (+10% MAP gains), and that it outperforms strong
baselines also relying on query expansion (LM+RM3). This conceptually simple
approach can easily be implemented on any IR system thanks to the availability
of GPT code and models.
