  The goal of information retrieval is to recommend a list of document
candidates that are most relevant to a given query. Listwise learning trains
neural retrieval models by comparing various candidates simultaneously on a
large scale, offering much more competitive performance than pairwise and
pointwise schemes. Existing listwise ranking losses treat the candidate
document list as a whole unit without further inspection. Some candidates with
moderate semantic prominence may be ignored by the noisy similarity signals or
overshadowed by a few especially pronounced candidates. As a result, existing
ranking losses fail to exploit the full potential of neural retrieval models.
To address these concerns, we apply the classic pooling technique to conduct
multi-level coarse graining and propose ExpertRank, a novel expert-based
listwise ranking loss. The proposed scheme has three major advantages: (1)
ExpertRank introduces the profound physics concept of coarse graining to
information retrieval by selecting prominent candidates at various local levels
based on model prediction and inter-document comparison. (2) ExpertRank applies
the mixture of experts (MoE) technique to combine different experts effectively
by extending the traditional ListNet. (3) Compared to other existing listwise
learning approaches, ExpertRank produces much more reliable and competitive
performance for various neural retrieval models with different complexities,
from traditional models, such as KNRM, ConvKNRM, MatchPyramid, to sophisticated
BERT/ALBERT-based retrieval models.
