@inproceedings{chakravarti-etal-2019-cfo,
title = "{CFO}: A Framework for Building Production {NLP} Systems",
author = "Chakravarti, Rishav and
Pendus, Cezar and
Sakrajda, Andrzej and
Ferritto, Anthony and
Pan, Lin and
Glass, Michael and
Castelli, Vittorio and
Murdock, J. William and
Florian, Radu and
Roukos, Salim and
Sil, Avi",
editor = "Pad{\'o}, Sebastian and
Huang, Ruihong",
booktitle = "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",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-3006",
doi = "10.18653/v1/D19-3006",
pages = "31--36",
abstract = "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 ({\textless} 3 min): http: //ibm.biz/gaama{\_}demo - Supplementary long video ({\textless} 13 min): \url{http://ibm.biz/gaama_cfo_demo}",
}
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<abstract>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 (\textless 3 min): http: //ibm.biz/gaama_demo - Supplementary long video (\textless 13 min): http://ibm.biz/gaama_cfo_demo</abstract>
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%0 Conference Proceedings
%T CFO: A Framework for Building Production NLP Systems
%A Chakravarti, Rishav
%A Pendus, Cezar
%A Sakrajda, Andrzej
%A Ferritto, Anthony
%A Pan, Lin
%A Glass, Michael
%A Castelli, Vittorio
%A Murdock, J. William
%A Florian, Radu
%A Roukos, Salim
%A Sil, Avi
%Y Padó, Sebastian
%Y Huang, Ruihong
%S 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
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F chakravarti-etal-2019-cfo
%X 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 (\textless 3 min): http: //ibm.biz/gaama_demo - Supplementary long video (\textless 13 min): http://ibm.biz/gaama_cfo_demo
%R 10.18653/v1/D19-3006
%U https://aclanthology.org/D19-3006
%U https://doi.org/10.18653/v1/D19-3006
%P 31-36
Markdown (Informal)
[CFO: A Framework for Building Production NLP Systems](https://aclanthology.org/D19-3006) (Chakravarti et al., EMNLP-IJCNLP 2019)
ACL
- Rishav Chakravarti, Cezar Pendus, Andrzej Sakrajda, Anthony Ferritto, Lin Pan, Michael Glass, Vittorio Castelli, J. William Murdock, Radu Florian, Salim Roukos, and Avi Sil. 2019. CFO: A Framework for Building Production NLP Systems. In 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, pages 31–36, Hong Kong, China. Association for Computational Linguistics.