论文标题
MTLB结构@Parseme 2020：使用多任务学习和预训练的蒙版语言模型捕获看不见的多词表达式
MTLB-STRUCT @PARSEME 2020: Capturing Unseen Multiword Expressions Using Multi-task Learning and Pre-trained Masked Language Models
论文作者
论文摘要
本文描述了一个半监督的系统，该系统共同学习了口头多字表达式（VMWES）和依赖性解析树作为辅助任务。该模型受益于预训练的多语言BERT。 Bert隐藏的图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。依赖性解析树的预测由线性层和双线性层建模，以及Bert顶部的树CRF。该系统已经参加了Parseme共享任务2020的开放式轨道，并在识别看不见的VMWES和VMWES方面排名第一，在所有14种语言中平均。
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.
