Enriching Deep Learning (DL) & ML with Apache Solr for Questing Answering (QA) System

Information Retrieval (IR) based question answering systems have many applications in the real world. Recent advances in DL give us a huge possibility to improve IR apps and engines, and allow us to incorporate systems like QA, chatbots etc. In this talk we will dive into our study on comparing traditional ML models vs DL models (both supervised and unsupervised) for different QA tasks such as answer paragraph selection, question-question similarity (FAQ matching) and answer span selection, and discuss the pros and cons of each method. For instance, using modern state-of-the-art DL models is quite expensive and cannot be easily scaled, thus we will present how to leverage Apache Solr's overlapping tokens and indexes to improve runtime performance of DL and other ML models.