Recent studies have shown that neural models can achieve high performance on several sequence labelling/tagging problems without the explicit use of linguistic features such as part-of-speech (POS) tags. These models are trained only using the character-level and the word embedding vectors as inputs. Others have shown that linguistic features can improve the performance of neural models on tasks such as chunking and named entity recognition (NER). However, the change in performance depends on the degree of semantic relatedness between the linguistic features and the target task; in some instances, linguistic features can have a negative impact on performance. This paper presents an approach to jointly learn these linguistic features along with the target sequence labelling tasks with a new multi-task learning (MTL) framework called Gated Tasks Interaction (GTI) network for solving multiple sequence tagging tasks. The GTI network exploits the relations between the multiple tasks via neural gate modules. These gate modules control the flow of information between the different tasks. Experiments on benchmark datasets for chunking and NER show that our framework outperforms other competitive baselines trained with and without external training resources.
翻译:最近的研究显示,神经模型可以在不明确使用语言特征(如部分语音标记)的情况下,在若干顺序标签/标记/标记问题上取得高性能;这些模型只使用字符级和嵌入矢量的字词作为投入来进行培训;其他的研究表明,语言特征可以改善神经模型在诸如块状和名称实体识别等任务方面的性能;然而,性能的变化取决于语言特征与目标任务之间的语义关联程度;在某些情况下,语言特征可能对绩效产生消极影响;本文件介绍了一种方法,即结合目标顺序标签任务(包括称为多任务学习框架(MTTL))与新的多任务学习框架(称为Gated任务互动网络)共同学习这些语言特征,以解决多序列标记任务。GTI网络通过神经门模块利用多种任务之间的关系。这些门模块控制不同任务之间的信息流动。关于块状和NER的基准数据集的实验表明,我们的框架超越了经过外部培训且没有外部培训资源的其他竞争性基线。