  We present NEAMER -- Named Entity Augmented Multi-word Expression Recognizer.
This system is inspired by non-compositionality characteristics shared between
Named Entity and Idiomatic Expressions. We utilize transfer learning and
locality features to enhance idiom classification task. This system is our
submission for SemEval Task 2: Multilingual Idiomaticity Detection and Sentence
Embedding Subtask A OneShot shared task. We achieve SOTA with F1 0.9395 during
post-evaluation phase. We also observe improvement in training stability.
Lastly, we experiment with non-compositionality knowledge transfer,
cross-lingual fine-tuning and locality features, which we also introduce in
this paper.
