  Deep neural models, in particular Transformer-based pre-trained language
models, require a significant amount of data to train. This need for data tends
to lead to problems when dealing with idiomatic multiword expressions (MWEs),
which are inherently less frequent in natural text. As such, this work explores
sample efficient methods of idiomaticity detection. In particular we study the
impact of Pattern Exploit Training (PET), a few-shot method of classification,
and BERTRAM, an efficient method of creating contextual embeddings, on the task
of idiomaticity detection. In addition, to further explore generalisability, we
focus on the identification of MWEs not present in the training data. Our
experiments show that while these methods improve performance on English, they
are much less effective on Portuguese and Galician, leading to an overall
performance about on par with vanilla mBERT. Regardless, we believe sample
efficient methods for both identifying and representing potentially idiomatic
MWEs are very encouraging and hold significant potential for future
exploration.
