How Good is the Model in Model-in-the-loop Event Coreference Resolution Annotation?
This addresses annotation efficiency and quality for researchers and practitioners in natural language processing, but it is incremental as it builds on existing model-in-the-loop methods.
The paper tackles the problem of annotating cross-document event coreference links, which is time-consuming and cognitively demanding, by proposing a model-in-the-loop approach that suggests likely coreferring event pairs, achieving 97% recall while reducing workload compared to manual annotation.
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