  We study Label Smoothing (LS), a widely used regularization technique, in the
context of neural learning to rank (L2R) models. LS combines the ground-truth
labels with a uniform distribution, encouraging the model to be less confident
in its predictions. We analyze the relationship between the non-relevant
documents-specifically how they are sampled-and the effectiveness of LS,
discussing how LS can be capturing "hidden similarity knowledge" between the
relevantand non-relevant document classes. We further analyze LS by testing if
a curriculum-learning approach, i.e., starting with LS and after anumber of
iterations using only ground-truth labels, is beneficial. Inspired by our
investigation of LS in the context of neural L2R models, we propose a novel
technique called Weakly Supervised Label Smoothing (WSLS) that takes advantage
of the retrieval scores of the negative sampled documents as a weak supervision
signal in the process of modifying the ground-truth labels. WSLS is simple to
implement, requiring no modification to the neural ranker architecture. Our
experiments across three retrieval tasks-passage retrieval, similar question
retrieval and conversation response ranking-show that WSLS for pointwise
BERT-based rankers leads to consistent effectiveness gains. The source code is
available at
https://anonymous.4open.science/r/dac85d48-6f71-4261-a7d8-040da6021c52/.
