  Expert search aims to find and rank experts based on a user's query. In
academia, retrieving experts is an efficient way to navigate through a large
amount of academic knowledge. Here, we study how different distributed
representations of academic papers (i.e. embeddings) impact academic expert
retrieval. We use the Microsoft Academic Graph dataset and experiment with
different configurations of a document-centric voting model for retrieval. In
particular, we explore the impact of the use of contextualized embeddings on
search performance. We also present results for paper embeddings that
incorporate citation information through retrofitting. Additionally,
experiments are conducted using different techniques for assigning author
weights based on author order. We observe that using contextual embeddings
produced by a transformer model trained for sentence similarity tasks produces
the most effective paper representations for document-centric expert retrieval.
However, retrofitting the paper embeddings and using elaborate author
contribution weighting strategies did not improve retrieval performance.
