Multiword expressions are a key ingredient for developing large-scale and linguistically sound natural language processing technology. This paper describes our improvements in automatically identifying Romanian multiword expressions on the corpus released for the PARSEME v1.2 shared task. Our approach assumes a multilingual perspective based on the recently introduced lateral inhibition layer and adversarial training to boost the performance of the employed multilingual language models. With the help of these two methods, we improve the F1-score of XLM-RoBERTa by approximately 2.7% on unseen multiword expressions, the main task of the PARSEME 1.2 edition. In addition, our results can be considered SOTA performance, as they outperform the previous results on Romanian obtained by the participants in this competition.

翻译：多词表达是开发大规模且语言学上合理的自然语言处理技术的关键要素。本文描述了我们在PARSEME v1.2共享任务发布的语料库上自动识别罗马尼亚语多词表达方面的改进。我们的方法基于最近引入的侧向抑制层与对抗训练，采用多语言视角以提升所使用的多语言语言模型的性能。借助这两种方法，我们将XLM-RoBERTa在未见过的多词表达上的F1分数提高了约2.7%，这是PARSEME 1.2版本的主要任务。此外，我们的结果可被视为当前最优性能，因为它们超越了该竞赛中参与者此前在罗马尼亚语上取得的结果。