  Automatically inducing high quality knowledge graphs from a given collection
of documents still remains a challenging problem in AI. One way to make headway
for this problem is through advancements in a related task known as slot
filling. In this task, given an entity query in form of [Entity, Slot, ?], a
system is asked to fill the slot by generating or extracting the missing value
exploiting evidence extracted from relevant passage(s) in the given document
collection. The recent works in the field try to solve this task in an
end-to-end fashion using retrieval-based language models. In this paper, we
present a novel approach to zero-shot slot filling that extends dense passage
retrieval with hard negatives and robust training procedures for retrieval
augmented generation models. Our model reports large improvements on both T-REx
and zsRE slot filling datasets, improving both passage retrieval and slot value
generation, and ranking at the top-1 position in the KILT leaderboard.
Moreover, we demonstrate the robustness of our system showing its domain
adaptation capability on a new variant of the TACRED dataset for slot filling,
through a combination of zero/few-shot learning. We release the source code and
pre-trained models.
