  Community Question Answering (CQA) has gained increasing popularity in many
domains. Mortgage is a complex and dynamic industry, and a flexible and
efficient CQA platform can potentially enhance the quality of service for
mortgage officers significantly. We have built a dynamic CQA platform with a
state of the art semantic search engine based on recent Natural Language
Processing (NLP) techniques to dynamically and collectively capture and
transfer the maturity and tribal knowledge of the more experienced workforce to
less experienced ones. The search engine allows for both keyword and natural
language queries and is based on a fine-tuned domain-adapted Sentence-BERT
encoder linearly composed with a TF-IDF vectorizer, and reciprocal-rank fused
with a BM25 vectorizer. Domain adaptation and fine-tuning is based on publicly
available mortgage corpora. Evaluation is performed on an internally annotated
dataset using standard information retrieval metrics such as normalized
discounted cumulative gain (nDCG), precision/recall at n, mean reciprocal rank,
and mean average precision (MAP). The results indicate that our hybrid,
fine-tuned, domain-adapted search engine is a more effective approach in
responding to the information needs of our mortgage officers compared to
traditional search techniques. We aim to publish the internally-annotated
evaluation and training datasets in the near future.
