Dialogue systems can benefit from being able to search through a corpus of text to find information relevant to user requests, especially when encountering a request for which no manually curated response is available. The state-of-the-art technology for neural dense retrieval or re-ranking involves deep learning models with hundreds of millions of parameters. However, it is difficult and expensive to get such models to operate at an industrial scale, especially for cloud services that often need to support a big number of individually customized dialogue systems, each with its own text corpus. We report our work on enabling advanced neural dense retrieval systems to operate effectively at scale on relatively inexpensive hardware. We compare with leading alternative industrial solutions and show that we can provide a solution that is effective, fast, and cost-efficient.

We introduce ARES (A Reading Comprehension Ensembling Service): a novel Machine Reading Comprehension (MRC) demonstration system which utilizes an ensemble of models to increase F1 by 2.3 points. While many of the top leaderboard submissions in popular MRC benchmarks such as the Stanford Question Answering Dataset (SQuAD) and Natural Questions (NQ) use model ensembles, the accompanying papers do not publish their ensembling strategies. In this work, we detail and evaluate various ensembling strategies using the NQ dataset. ARES leverages the CFO (Chakravarti et al., 2019) and ReactJS distributed frameworks to provide a scalable interactive Question Answering experience that capitalizes on the agreement (or lack thereof) between models to improve the answer visualization experience.

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 Com- prehension) 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. Screencast links: - Short video (< 3 min): http: //ibm.biz/gaama_demo - Supplementary long video (< 13 min): http://ibm.biz/gaama_cfo_demo