Reviews: Multitask Spectral Learning of Weighted Automata

SUMMARY The paper studies the problem of multitask learning of WFAs. It defines a notion of relatedness among tasks, and designs a new algorithm that can exploit such relatedness. Roughly speaking, the new algorithm stacks the Hankel matrices from different tasks together and perform an adapted version of spectral learning, resulting in a vv-WFA that can make vector-valued predictions with a unified state representation. A post-processing step that reduces the dimension of the WFA for each single task is also suggested to reduce noise. The algorithm is compared to the baseline of learning each task separately on both synthetic and real-world data.