“越南语观点对象识别是越南语事件观点分析的重要研究内容。由于汉越两种语言的语法结构上存在差异,使得多语言事件关联复杂,观点对象表征困难。现有研究方法仅能实现汉越双语的表征,未能有效捕获并利用汉越双语事件中要素的关联关系。因此,本文提出一种融合汉越关联关系的多语言事件观点对象识别方法,利用中文和越南语事件间的要素共现和整体语义关联构建汉越多语言事件表征网络,使用多语言预训练语言模型获得要素节点的特征向量,利用图卷积网络对节点信息进行聚合,得到同一语义空间下汉越双语的公共表征,实现汉越事件观点对象的识别。实验结果表明本文模型能够更有效地构建多语言关联信息,其F1值较多个基线模型都有明显提高。”

“多模态神经机器翻译旨在利用视觉信息来提高文本翻译质量。传统多模态机器翻译将图像的全局语义信息融入到翻译模型,而忽略了图像的细粒度信息对翻译质量的影响。对此,该文提出一种基于图文细粒度对齐语义引导的多模态神经机器翻译方法,该方法首先跨模态交互图文信息,以提取图文细粒度对齐语义信息,然后以图文细粒度对齐语义信息为枢纽,采用门控机制将多模态细粒度信息对齐到文本信息上,实现图文多模态特征融合。在多模态机器翻译基准数据集Multi30K 英语→德语、英语→法语以及英语→捷克语翻译任务上的实验结果表明,论文提出方法的有效性,并且优于大多数最先进的多模态机器翻译方法。”

Multi-modal neural machine translation (MNMT) aims to improve textual level machine translation performance in the presence of text-related images. Most of the previous works on MNMT focus on multi-modal fusion methods with full visual features. However, text and its corresponding image may not match exactly, visual noise is generally inevitable. The irrelevant image regions may mislead or distract the textual attention and cause model performance degradation. This paper proposes a noise-robust multi-modal interactive fusion approach with cross-modal relation-aware mask mechanism for MNMT. A text-image relation-aware attention module is constructed through the cross-modal interaction mask mechanism, and visual features are extracted based on the text-image interaction mask knowledge. Then a noise-robust multi-modal adaptive fusion approach is presented by fusion the relevant visual and textual features for machine translation. We validate our method on the Multi30K dataset. The experimental results show the superiority of our proposed model, and achieve the state-of-the-art scores in all En-De, En-Fr and En-Cs translation tasks.