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Motivated by the emerging demand in the financial industry for the automaticanalysis of unstructured and structured data at scale, Question Answering (QA)systems can provide lucrative and competitive advantages to companies byfacilitating the decision making of financial advisers. Consequently, wepropose a novel financial QA system using the transformer-based pre-trainedBERT language model to address the limitations of data scarcity and languagespecificity in the financial domain. Our system focuses on financialnon-factoid answer selection, which retrieves a set of passage-level texts andselects the most relevant as the answer. To increase efficiency, we formulatethe answer selection task as a re-ranking problem, in which our system consistsof an Answer Retriever using BM25, a simple information retrieval approach, tofirst return a list of candidate answers, and an Answer Re-ranker built withvariants of pre-trained BERT language models to re-rank and select the mostrelevant answers. We investigate various learning, further pre-training, andfine-tuning approaches for BERT. Our experiments suggest that FinBERT-QA, amodel built from applying the Transfer and Adapt further fine-tuning andpointwise learning approach, is the most effective, improving thestate-of-the-art results of task 2 of the FiQA dataset by 16% on MRR, 17% onNDCG, and 21% on Precision@1.

FinBERT-QA: Financial Question Answering with pre-trained BERT Language Models