We investigate the recently developed Bidirectional Encoder Representations from Transformers (BERT) model (Devlin et al., 2018) for the hyperpartisan news detection task. Using a subset of hand-labeled articles from Se-mEval as a validation set, we test the performance of different parameters for BERT models. We find that accuracy from two different BERT models using different proportions of the articles is consistently high, with our bestperforming model on the validation set achieving 85% accuracy and the best-performing model on the test set achieving 77%. We further determined that our model exhibits strong consistency, labeling independent slices of the same article identically. Finally, we find that randomizing the order of word pieces dramatically reduces validation accuracy (to approximately 60%), but that shuffling groups of four or more word pieces maintains an accuracy of about 80%, indicating the model mainly gains value from local context.

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Flexibility is a useful and common metric for measuring the amount of slack in a Simple Temporal Network (STN) solution space. We extend this concept to specific schedules within an STN’s solution space, developing a related notion of durability that captures an individual schedule’s ability to withstand disturbances and still remain valid. We identify practical sources of scheduling disturbances that motivate the need for durable schedules, and create a geometricallyinspired empirical model that enables testing a given schedule’s ability to withstand these disturbances. We develop a number of durability metrics and use these to characterize and compute specific schedules that we expect to have high durability. Using our model of disturbances, we show that our durability metrics strongly predict a schedule’s resilience to practical scheduling disturbances. We also demonstrate that the schedules we identify as having high durability are up to three times more resilient to disturbances than an arbitrarily chosen schedule is.