Using transformers to improve answer retrieval for legal questions
Andrew Vold, Jack G. Conrad · 2021
Transformer architectures such as BERT, XLNet, and others are frequently used in the field of natural language processing. Transformers have achieved state-of-the-art performance in tasks such as text classification, passage summarization, machine translation, and question answering. Efficient hosting of transformer models, however, is a difficult task because of their large size and high latency. In this work, we describe how we deploy a RoBERTa Base question answer classification model in a production environment. We also compare the answer retrieval performance of a RoBERTa Base classifier against a traditional machine learning model in the legal domain by measuring the performance difference between a trained linear SVM on the publicly available PRIVACYQA dataset. We show that RoBERTa achieves a 31% improvement in F1-score and a 41% improvement in Mean Reciprocal Rank over the traditional SVM.