kpfriends at S em E val-2022 Task 2: NEAMER - Named Entity Augmented Multi-word Expression Recognizer (2022)

Abstract: 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.

## introduction multi-word expressions (mwes) are defined as "idiosyncratic interpretations that cross word boundaries (or spaces)" @xcite . recent advances in pre-trained language models such as bert @xcite have enhanced performance of sentence classification task, however tasks that specifically identify multi-word expressions (mwe) remain unsolved due to its specific idiomatic properties @xcite . this semeval shared task @xcite aims to understand multi-word expressions better by novel classification and sentence similarity tasks. named entity recognition (ner) is a task to identify named entities (people, organizations etc.) in a sentence. multiple datasets exist that specifically perform this task, including conll-02/03 shared tasks for english, german, spanish and dutch @xcite . multi-word expressions and named entities are similar in a 0 (idiomatic) gold mine the hashtag "qixia gold mine incident" has been viewed many million of times on the social media site weibo. we propose neamer -named entity augmented multi-word expression recognizer that aim to utilize non-compositionality shared between two streams of nlp research. we explore transfer learning between ner and idiom classification tasks. we also experiment with "locality features" to augment representations of text. we have participated in subtask a which is a multilingual classification task to determine if a given sentence has correct idiomatic usage or not. we have focused our efforts on the oneshot setting, where the goal is to classify the target sentence utilizing the zeroshot dataset consisting of idioms not found in test set and the oneshot dataset consisting of 1 idiom-label pair for all idioms in test set. the dataset has been provided by task organizers @xcite . contributions of this paper are : ## conclusion we present neamer -named entity augmented multi-word expression recognizer. this system explores how we can utilize non-compositionality shared between named entity and idiomatic expressions. we find that the ner transfer learning variant achieves the best mwe classification oneshot performance. we also observe high training stability. we investigate non-compositionality knowledge transfer between tasks and obtain promising results across experiments.