  Most approaches for similar text retrieval and ranking with long natural
language queries rely at some level on queries and responses having words in
common with each other. Recent applications of transformer-based neural
language models to text retrieval and ranking problems have been very
promising, but still involve a two-step process in which result candidates are
first obtained through bag-of-words-based approaches, and then reranked by a
neural transformer. In this paper, we introduce novel approaches for
effectively applying neural transformer models to similar text retrieval and
ranking without an initial bag-of-words-based step. By eliminating the
bag-of-words-based step, our approach is able to accurately retrieve and rank
results even when they have no non-stopwords in common with the query. We
accomplish this by using bidirectional encoder representations from
transformers (BERT) to create vectorized representations of sentence-length
texts, along with a vector nearest neighbor search index. We demonstrate both
supervised and unsupervised means of using BERT to accomplish this task.
