Video Recordings

I will present two recent works centered around multilingual zero-shot transfer, which occurs when models can solve instances without direct supervision in their target language. First, I will present a model capable of filling in eroded parts in ancient cuneiform tablets written thousands of years ago in Akkadian. We find that zero-shot models do better than monolingual models given the limited training data available for this task, and show their effectiveness in automatic and human evaluations. Motivated by these findings, I will present an experiment of zero-shot performance under balanced data conditions which mitigate corpus size confounds. We show that the choice of pretraining languages vastly affects downstream cross-lingual transfer for BERT-based models, and develop a method of quadratic time complexity in the number of pretraining languages to estimate these inter-language relations. Our findings can inform pretraining configurations in future large-scale multilingual language models. This work was recently awarded an outstanding paper award at NAACL 2022.