论文标题
多语言语言翻译和发电的知识图
Knowledge Graphs for Multilingual Language Translation and Generation
论文作者
论文摘要
自然语言处理（NLP）社区最近看到了出色的进步，这是由于不同神经网络（NN）架构的释放而催化的。事实证明，基于神经的方法通过显着提高了NLP任务的大量自动化解决方案的产出质量（Belinkov and Glass，2019）。尽管有这些显着的进步，但与实体打交道仍然构成了艰难的挑战，因为它们在培训数据中很少见。实体可以分为两组，即专有名词和通用名词。专有名词也称为命名实体（NE），与人，组织或位置的名称相对应，例如John，Who或Canada。常见名词描述了物体类别，例如勺子或癌症。两种类型的实体都可以在知识图（kg）中找到。最近的工作已成功利用了KGS在NLP任务中的贡献，例如自然语言推论（NLI）（KM等，2018）和问题回答（QA）（Sorokin和Gurevych，2018）。当此处介绍的工作开始时，只有少数几幅作品利用了KGS在神经机器翻译（NMT）中的好处。此外，很少有作品研究KGS对自然语言生成（NLG）任务的贡献。此外，多语言在这些任务中仍然是一个开放的研究领域（Young等，2018）。在本文中，我们着重于将kgs用于机器翻译和生成文本来处理由实体引起的问题，从而提高了自动生成的文本的质量。
The Natural Language Processing (NLP) community has recently seen outstanding progress, catalysed by the release of different Neural Network (NN) architectures. Neural-based approaches have proven effective by significantly increasing the output quality of a large number of automated solutions for NLP tasks (Belinkov and Glass, 2019). Despite these notable advancements, dealing with entities still poses a difficult challenge as they are rarely seen in training data. Entities can be classified into two groups, i.e., proper nouns and common nouns. Proper nouns are also known as Named Entities (NE) and correspond to the name of people, organizations, or locations, e.g., John, WHO, or Canada. Common nouns describe classes of objects, e.g., spoon or cancer. Both types of entities can be found in a Knowledge Graph (KG). Recent work has successfully exploited the contribution of KGs in NLP tasks, such as Natural Language Inference (NLI) (KM et al.,2018) and Question Answering (QA) (Sorokin and Gurevych, 2018). Only a few works had exploited the benefits of KGs in Neural Machine Translation (NMT) when the work presented herein began. Additionally, few works had studied the contribution of KGs to Natural Language Generation (NLG) tasks. Moreover, the multilinguality also remained an open research area in these respective tasks (Young et al., 2018). In this thesis, we focus on the use of KGs for machine translation and the generation of texts to deal with the problems caused by entities and consequently enhance the quality of automatically generated texts.
