Optimal selection of a subset of items from a given set is a hard problem that requires combinatorial optimization. In this paper, we propose a subset selection algorithm that is trainable with gradient based methods yet achieves near optimal performance via submodular optimization. We focus on the task of identifying a relevant set of sentences for claim verification in the context of the FEVER task. Conventional methods for this task look at sentences on their individual merit and thus do not optimize the informativeness of sentences as a set. We show that our proposed method which builds on the idea of unfolding a greedy algorithm into a computational graph allows both interpretability and gradient based training. The proposed differentiable greedy network (DGN) outperforms discrete optimization algorithms as well as other baseline methods in terms of precision and recall.
We propose a subset selection algorithm that is trainable with gradient based methods yet achieves near optimal performance via submodular optimization. Proposes a neural network based model that integrates submodular function by combining gradient based optimization technique with submodular framework named 'Differentiable Greedy Network' (DGN). Proposes a neural network that aims to select a subset of elements (e.g. selecting k sentences that are mostly related to a claim from a set of retrieved docs)