面向抵押贷款人员的社区问答语义搜索
Towards Semantic Search for Community Question Answering for Mortgage Officers
摘要：
社区问答（CQA）在许多领域越来越受欢迎。抵押贷款是一个复杂而动态的行业，灵活而高效的CQA平台可以显著提高抵押贷款人员的服务质量。我们已经建立了一个动态CQA平台，该平台具有最先进的语义搜索引擎，该引擎基于最新的自然语言处理（NLP）技术，可以动态地、集体地捕获并将经验丰富的员工的成熟度和部落知识转移给经验不足的员工。该搜索引擎允许关键字和自然语言查询，并基于一个微调域自适应句子BERT编码器线性组成的TF-IDF矢量化，和倒数秩融合BM 25矢量化。领域自适应和微调基于公开可用的抵押贷款语料库。使用标准的信息检索指标，如归一化的折扣累积增益（nDCG），精度/召回率在n，平均倒数排名，平均平均精度（MAP）的内部注释的数据集上进行评估。结果表明，我们的混合，微调，域适应搜索引擎是一个更有效的方法，在回应我们的抵押贷款人员的信息需求相比，传统的搜索技术。我们的目标是在不久的将来发布内部注释的评估和培训数据集。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
