  In this paper we explore the effects of negative sampling in dual encoder
models used to retrieve passages for automatic question answering. We explore
four negative sampling strategies that complement the straightforward random
sampling of negatives, typically used to train dual encoder models. Out of the
four strategies, three are based on retrieval and one on heuristics. Our
retrieval-based strategies are based on the semantic similarity and the lexical
overlap between questions and passages. We train the dual encoder models in two
stages: pre-training with synthetic data and fine tuning with domain-specific
data. We apply negative sampling to both stages. The approach is evaluated in
two passage retrieval tasks. Even though it is not evident that there is one
single sampling strategy that works best in all the tasks, it is clear that our
strategies contribute to improving the contrast between the response and all
the other passages. Furthermore, mixing the negatives from different strategies
achieve performance on par with the best performing strategy in all tasks. Our
results establish a new state-of-the-art level of performance on two of the
open-domain question answering datasets that we evaluated.
