  Recently, the NLP community has witnessed a rapid advancement in multilingual
and cross-lingual transfer research where the supervision is transferred from
high-resource languages (HRLs) to low-resource languages (LRLs). However, the
cross-lingual transfer is not uniform across languages, particularly in the
zero-shot setting. Towards this goal, one promising research direction is to
learn shareable structures across multiple tasks with limited annotated data.
The downstream multilingual applications may benefit from such a learning setup
as most of the languages across the globe are low-resource and share some
structures with other languages. In this paper, we propose a novel
meta-learning framework (called Meta-X$_{NLG}$) to learn shareable structures
from typologically diverse languages based on meta-learning and language
clustering. This is a step towards uniform cross-lingual transfer for unseen
languages. We first cluster the languages based on language representations and
identify the centroid language of each cluster. Then, a meta-learning algorithm
is trained with all centroid languages and evaluated on the other languages in
the zero-shot setting. We demonstrate the effectiveness of this modeling on two
NLG tasks (Abstractive Text Summarization and Question Generation), 5 popular
datasets and 30 typologically diverse languages. Consistent improvements over
strong baselines demonstrate the efficacy of the proposed framework. The
careful design of the model makes this end-to-end NLG setup less vulnerable to
the accidental translation problem, which is a prominent concern in zero-shot
cross-lingual NLG tasks.

Computation and Language