Atif Ahmed


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A Data-Centric Framework for Composable NLP Workflows
Zhengzhong Liu | Guanxiong Ding | Avinash Bukkittu | Mansi Gupta | Pengzhi Gao | Atif Ahmed | Shikun Zhang | Xin Gao | Swapnil Singhavi | Linwei Li | Wei Wei | Zecong Hu | Haoran Shi | Xiaodan Liang | Teruko Mitamura | Eric Xing | Zhiting Hu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Empirical natural language processing (NLP) systems in application domains (e.g., healthcare, finance, education) involve interoperation among multiple components, ranging from data ingestion, human annotation, to text retrieval, analysis, generation, and visualization. We establish a unified open-source framework to support fast development of such sophisticated NLP workflows in a composable manner. The framework introduces a uniform data representation to encode heterogeneous results by a wide range of NLP tasks. It offers a large repository of processors for NLP tasks, visualization, and annotation, which can be easily assembled with full interoperability under the unified representation. The highly extensible framework allows plugging in custom processors from external off-the-shelf NLP and deep learning libraries. The whole framework is delivered through two modularized yet integratable open-source projects, namely Forte (for workflow infrastructure and NLP function processors) and Stave (for user interaction, visualization, and annotation).