  This paper explores new methods for locating the sources used to write a
text, by fine-tuning a variety of language models to rerank candidate sources.
After retrieving candidates sources using a baseline BM25 retrieval model, a
variety of reranking methods are tested to see how effective they are at the
task of source attribution. We conduct experiments on two datasets, English
Wikipedia and medieval Arabic historical writing, and employ a variety of
retrieval and generation based reranking models. In particular, we seek to
understand how the degree of supervision required affects the performance of
various reranking models. We find that semisupervised methods can be nearly as
effective as fully supervised methods while avoiding potentially costly
span-level annotation of the target and source documents.
