  Adapter modules, additional trainable parameters that enable efficient
fine-tuning of pretrained transformers, have recently been used for language
specialization of multilingual transformers, improving downstream zero-shot
cross-lingual transfer. In this work, we propose orthogonal language and task
adapters (dubbed orthoadapters) for cross-lingual transfer. They are trained to
encode language- and task-specific information that is complementary (i.e.,
orthogonal) to the knowledge already stored in the pretrained transformer's
parameters. Our zero-shot cross-lingual transfer experiments, involving three
tasks (POS-tagging, NER, NLI) and a set of 10 diverse languages, 1) point to
the usefulness of orthoadapters in cross-lingual transfer, especially for the
most complex NLI task, but also 2) indicate that the optimal adapter
configuration highly depends on the task and the target language. We hope that
our work will motivate a wider investigation of usefulness of orthogonality
constraints in language- and task-specific fine-tuning of pretrained
transformers.
