Monday, 10 October, 2022 - 14:00
Room: 

Multilingual Zero-Shot Transfer in Low-Resource Settings

Gabriel Stanovsky (Hebrew University of Jerusalem)

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.
 

CV: 

Dr. Gabriel Stanovsky is a senior lecturer (assistant professor) at the Hebrew University of Jerusalem and a research scientist at the Allen Institute for AI (AI2). He did his postdoctoral research at the University of Washington and AI2 in Seattle, working with Prof. Luke Zettlemoyer and Prof. Noah Smith, and his PhD with Prof. Ido Dagan at Bar-Ilan University. He is interested in developing natural language processing models which deal with real-world texts and help answer multi-disciplinary research questions, in archeology, law, medicine, and more.  His work has received awards at top-tier conferences, including ACL, NAACL, and CoNLL, and recognition in popular journals such as Science and The New York Times.