How Good is the Model in Model-in-the-loop Event Coreference Resolution Annotation?

• URL: http://arxiv.org/abs/2306.05434v1
• Date: Tue, 6 Jun 2023 18:06:24 GMT
• Title: How Good is the Model in Model-in-the-loop Event Coreference Resolution Annotation?
• Authors: Shafiuddin Rehan Ahmed, Abhijnan Nath, Michael Regan, Adam Pollins, Nikhil Krishnaswamy, James H. Martin
• Abstract summary: We propose a model-in-the-loop annotation approach for event coreference resolution, where a machine learning model suggests likely corefering event pairs only. We evaluate the effectiveness of this approach by first simulating the annotation process and then, using a novel annotator-centric Recall- effort trade-off metric, we compare the results of various underlying models and datasets.
• Score: 3.712417884848568
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Annotating cross-document event coreference links is a time-consuming and cognitively demanding task that can compromise annotation quality and efficiency. To address this, we propose a model-in-the-loop annotation approach for event coreference resolution, where a machine learning model suggests likely corefering event pairs only. We evaluate the effectiveness of this approach by first simulating the annotation process and then, using a novel annotator-centric Recall-Annotation effort trade-off metric, we compare the results of various underlying models and datasets. We finally present a method for obtaining 97\% recall while substantially reducing the workload required by a fully manual annotation process. Code and data can be found at https://github.com/ahmeshaf/model_in_coref

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