  In meta-learning, the knowledge learned from previous tasks is transferred to
new ones, but this transfer only works if tasks are related. Sharing
information between unrelated tasks might hurt performance, and it is unclear
how to transfer knowledge across tasks with a hierarchical structure. Our
research extends a model agnostic meta-learning model, MAML, by exploiting
hierarchical task relationships. Our algorithm, TreeMAML, adapts the model to
each task with a few gradient steps, but the adaptation follows the
hierarchical tree structure: in each step, gradients are pooled across tasks
clusters, and subsequent steps follow down the tree. We also implement a
clustering algorithm that generates the tasks tree without previous knowledge
of the task structure, allowing us to make use of implicit relationships
between the tasks. We show that the new algorithm, which we term TreeMAML,
performs better than MAML when the task structure is hierarchical for synthetic
experiments. To study the performance of the method in real-world data, we
apply this method to Natural Language Understanding, we use our algorithm to
finetune Language Models taking advantage of the language phylogenetic tree. We
show that TreeMAML improves the state of the art results for cross-lingual
Natural Language Inference. This result is useful, since most languages in the
world are under-resourced and the improvement on cross-lingual transfer allows
the internationalization of NLP models. This results open the window to use
this algorithm in other real-world hierarchical datasets.
