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Self- and semi-supervised learning methods have been actively investigated to reduce labeled training data or enhance the model performance. However, the approach mostly focus on in-domain performance for public datasets. In this study, we utilize the combination of self- and semi-supervised learning methods to solve unseen domain adaptation problem in a large-scale production setting for online ASR model.

Current Internet of Things (IoT) embedded applications use machine learning algorithms to process the collected data. However, the computational complexity and storage requirements of existing deep learning methods hinder the wide availability of embedded applications.
Spiking Neural Networks~(SNN) is a brain-inspired learning methodology that emerged from theoretical neuroscience, as an alternative computing paradigm for enabling low-power computation.

Misleading or false information has been creating chaos in some places around the world. To mitigate this issue, many researchers have proposed automated fact-checking methods to fight the spread of fake news. However, most methods cannot explain the reasoning behind their decisions, failing to build trust between machines and humans using such technology. Trust is essential for fact-checking to be applied in the real world. Here, we address fact-checking explainability through question answering.