This paper introduces a novel orchestration framework, called CFO (COMPUTATION FLOW ORCHESTRATOR), for building, experimenting with, and deploying interactive NLP (Natural Language Processing) and IR (Information Retrieval) systems to production environments. We then demonstrate a question answering system built using this framework which incorporates state-of-the-art BERT based MRC (Machine Reading Comprehension) with IR components to enable end-to-end answer retrieval. Results from the demo system are shown to be high quality in both academic and industry domain specific settings. Finally, we discuss best practices when (pre-)training BERT based MRC models for production systems.
@article{arxiv.1908.06121,
title = {CFO: A Framework for Building Production NLP Systems},
author = {Rishav Chakravarti and Cezar Pendus and Andrzej Sakrajda and Anthony Ferritto and Lin Pan and Michael Glass and Vittorio Castelli and J. William Murdock and Radu Florian and Salim Roukos and Avirup Sil},
journal= {arXiv preprint arXiv:1908.06121},
year = {2020}
}