kpfriends at SemEval-2022 Task 2: NEAMER -- Named Entity Augmented Multi-word Expression Recognizer
This work addresses the challenge of detecting idiomatic expressions in multiple languages, which is important for natural language processing applications, but it appears incremental as it builds on existing transfer learning and feature-based approaches.
The paper tackled the problem of multilingual idiomaticity detection by proposing NEAMER, a system that leverages shared non-compositionality characteristics between named entities and idiomatic expressions, achieving state-of-the-art results with an F1 score of 0.9395 in a post-evaluation phase.
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.