J. William Murdock


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ARES: A Reading Comprehension Ensembling Service
Anthony Ferritto | Lin Pan | Rishav Chakravarti | Salim Roukos | Radu Florian | J. William Murdock | Avi Sil
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

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the Stanford Question Answering Dataset (SQuAD) and Natural Questions (NQ) use model ensembles, the accompanying papers do not publish their ensembling strategies.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.


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CFO: A Framework for Building Production NLP Systems
Rishav Chakravarti | Cezar Pendus | Andrzej Sakrajda | Anthony Ferritto | Lin Pan | Michael Glass | Vittorio Castelli | J. William Murdock | Radu Florian | Salim Roukos | Avi Sil
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

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


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Multi-Dimensional Feature Merger for Question Answering
Apoorv Agarwal | J. William Murdock | Jennifer Chu-Carroll | Adam Lally | Aditya Kalyanpur
Proceedings of COLING 2012