Title: An Exploration of Approaches to Integrating Neural Reranking Models in Multi-Stage Ranking Architectures,
Abstract: We explore different approaches to integrating a simple convolutional neural
network (CNN) with the Lucene search engine in a multi-stage ranking
architecture. Our models are trained using the PyTorch deep learning toolkit,
which is implemented in C/C++ with a Python frontend. One obvious integration
strategy is to expose the neural network directly as a service. For this, we
use Apache Thrift, a software framework for building scalable cross-language
services. In exploring alternative architectures, we observe that once trained,
the feedforward evaluation of neural networks is quite straightforward.
Therefore, we can extract the parameters of a trained CNN from PyTorch and
import the model into Java, taking advantage of the Java Deeplearning4J library
for feedforward evaluation. This has the advantage that the entire end-to-end
system can be implemented in Java. As a third approach, we can extract the
neural network from PyTorch and "compile" it into a C++ program that exposes a
Thrift service. We evaluate these alternatives in terms of performance (latency
and throughput) as well as ease of integration. Experiments show that
feedforward evaluation of the convolutional neural network is significantly
slower in Java, while the performance of the compiled C++ network does not
consistently beat the PyTorch implementation.