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Thursday, October 18 • 2:55pm - 3:35pm
Enriching Solr with Deep Learning for a Question Answering System

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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 chatbots. In this talk we will show 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 Solr's payloads and indexes to improve runtime performance of DL and other ML models.


Savva Kolbachev

avatar for Sanket Shahane

Sanket Shahane

Research Engineer, Lucidworks
Sanket is the Research Engineer at Lucidworks Inc. passionate about machine learning and search. His focus of work involves researching and developing methodologies to solve complex problems of the search domain like Cold Start problem(in search context), developing Question Answering... Read More →

Thursday October 18, 2018 2:55pm - 3:35pm EDT
Salon 4&5