Automatically generating question answer (QA) pairs from the rapidly growing coronavirus-related literature is of great value to the medical community.
In response to the Kaggle's COVID-19 Open Research Dataset (CORD-19) challenge, we have proposed three transformer-based question-answering systems using BERT, ALBERT, and T5 models.
On its own, improvements with StackMix hold across different number of labeled samples on CIFAR-100, maintaining approximately a 2\% gap in test accuracy -- down to using only 5\% of the whole dataset -- and is effective in the semi-supervised setting with a 2\% improvement with the standard benchmark $\Pi$-model.
Clinical machine learning is increasingly multimodal, collected in both structured tabular formats and unstructured forms such as freetext.
We show, both theoretically and empirically, that our proposed solution is significantly superior for load balancing and is optimal in many senses.
Data Structures and Algorithms
With the resurgence of chat-based dialog systems in consumer and enterprise applications, there has been much success in developing data-driven and rule-based natural language models to understand human intent.
With the increasing number of communication platforms that offer variety of ways of connecting two interlocutors, there is a resurgence of chat-based dialog systems.