  Our work aimed at experimentally assessing the benefits of model ensembling
within the context of neural methods for passage reranking. Starting from
relatively standard neural models, we use a previous technique named Fast
Geometric Ensembling to generate multiple model instances from particular
training schedules, then focusing or attention on different types of approaches
for combining the results from the multiple model instances (e.g., averaging
the ranking scores, using fusion methods from the IR literature, or using
supervised learning-to-rank). Tests with the MS-MARCO dataset show that model
ensembling can indeed benefit the ranking quality, particularly with supervised
learning-to-rank although also with unsupervised rank aggregation.
