Flexible IR Pipelines with Capreolus

While a number of recent open-source toolkits for training and using neural information retrieval models have greatly simplified experiments with neural reranking methods, they essentially hard code a "search-then-rerank'' experimental pipeline. These pipelines consist of an efficient first-stage ranking method, like BM25, followed by a neural reranking method. Deviations from this setup often require hacks; some improvements, like adding a second reranking step that uses a more expensive neural method, are infeasible without major code changes. In order to improve the flexibility of such toolkits, we propose implementing experimental pipelines as dependency graphs of functional "IR primitives,'' which we call modules, that can be used and combined as needed.