Combining Data-driven Supervision with Human-in-the-loop Feedback for Entity Resolution

• URL: http://arxiv.org/abs/2111.10497v1
• Date: Sat, 20 Nov 2021 02:22:12 GMT
• Title: Combining Data-driven Supervision with Human-in-the-loop Feedback for Entity Resolution
• Authors: Wenpeng Yin, Shelby Heinecke, Jia Li, Nitish Shirish Keskar, Michael Jones, Shouzhong Shi, Stanislav Georgiev, Kurt Milich, Joseph Esposito, Caiming Xiong
• Abstract summary: We build a model that identifies and consolidates data points that represent the same person. In this case study, we discuss our human-in-the-loop enabled, data-centric solution to closing the training-production performance divergence.
• Score: 47.90125404360125
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The distribution gap between training datasets and data encountered in production is well acknowledged. Training datasets are often constructed over a fixed period of time and by carefully curating the data to be labeled. Thus, training datasets may not contain all possible variations of data that could be encountered in real-world production environments. Tasked with building an entity resolution system - a model that identifies and consolidates data points that represent the same person - our first model exhibited a clear training-production performance gap. In this case study, we discuss our human-in-the-loop enabled, data-centric solution to closing the training-production performance divergence. We conclude with takeaways that apply to data-centric learning at large.

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