Assessing the Benefits of Model Ensembles in Neural Re-Ranking for Passage Retrieval
This work addresses the problem of improving retrieval accuracy in information retrieval systems, but it is incremental as it builds on existing ensembling techniques.
The study experimentally assessed the benefits of model ensembling for neural passage reranking, finding that it improves ranking quality, particularly with supervised learning-to-rank methods, as tested on the MS-MARCO dataset.
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.