MTLB-STRUCT @ PARSEME 2020：使用多任务学习和预训练的掩蔽语言模型捕获未看到的多词表达式
MTLB-STRUCT @PARSEME 2020: Capturing Unseen Multiword Expressions Using Multi-task Learning and Pre-trained Masked Language Models
摘要：
本文描述了一个半监督系统，共同学习口头多词表达（VMWEs）和依赖分析树作为辅助任务。该模型受益于预先训练的多语言BERT。BERT隐藏层在两个任务之间共享，我们引入了一个额外的线性层来检索VMWE标签。依赖分析树预测模型由一个线性层和一个双线性层加上一个树CRF的顶部BERT。该系统参与了PARSEME共享任务2020的开放式轨道，并在识别未见过的VMWE以及一般VMWE方面的F1得分方面排名第一，平均跨越所有14种语言。
英文摘要：
This paper describes a semi-supervised system that jointly learns verbal multiword expressions (VMWEs) and dependency parse trees as an auxiliary task. The model benefits from pre-trained multilingual BERT. BERT hidden layers are shared among the two tasks and we introduce an additional linear layer to retrieve VMWE tags. The dependency parse tree prediction is modelled by a linear layer and a bilinear one plus a tree CRF on top of BERT. The system has participated in the open track of the PARSEME shared task 2020 and ranked first in terms of F1-score in identifying unseen VMWEs as well as VMWEs in general, averaged across all 14 languages.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
