Title: Modeling Label Ambiguity for Neural List-Wise Learning to Rank,
Abstract: List-wise learning to rank methods are considered to be the state-of-the-art.
One of the major problems with these methods is that the ambiguous nature of
relevance labels in learning to rank data is ignored. Ambiguity of relevance
labels refers to the phenomenon that multiple documents may be assigned the
same relevance label for a given query, so that no preference order should be
learned for those documents. In this paper we propose a novel sampling
technique for computing a list-wise loss that can take into account this
ambiguity. We show the effectiveness of the proposed method by training a
3-layer deep neural network. We compare our new loss function to two strong
baselines: ListNet and ListMLE. We show that our method generalizes better and
significantly outperforms other methods on the validation and test sets.