Sentences, entities, and keyphrases extraction from consumer health forums using multi-task learning

We extended an Indonesian dataset for SR, MER, and KE tasks, resulted in 1, 173 labeled data points which splitted into 773 training instances, 200 validation instances, and 200 testing instances. We then used transformer-based models to set a baseline for all three tasks. Our MTL experiments suggested that additional information regarding the other two tasks could help the learning process for MER and KE tasks, while had only a small effect for SR task.