In our experiments our LRLs are all selected to have an helper that is highly similar in both aspects, but choosing an appropriate helper when this is not the case is an interesting problem for future work.
we contrast a number of strategies, leading to a novel, simple, yet effective method of "similar-language regularization", where we jointly train on both a LRL of interest and a similar high-resourced language to prevent over-fitting to small LRL data.