论文标题
多语言命名实体识别的转移来源
Sources of Transfer in Multilingual Named Entity Recognition
论文作者
论文摘要
命名的本质上是多语言的，任何给定语言的注释可能受到限制。这促使我们考虑了命名 - 实体识别（NER）的多声明，其中一种模型是使用从多种语言中绘制的带注释的数据训练的。但是，这种简单想法的直接实现并不总是在实践中起作用：使用从多种语言绘制的带注释的数据对NER模型进行幼稚培训，尽管可以访问更多培训数据，但始终不受培训单语数据的表现模型。本文的起点是解决此问题的一个简单解决方案，其中多插图模型在单语数据上进行了微调，以始终如一，显着优于其单语言对应物。为了解释这种现象，我们探讨了多语言模型中多语言转移的来源，并检查了与单语的同类物相比，多语言模型的重量结构。我们发现多面体模型有效地跨语言共享许多参数，并且微调可能会利用大量参数。
Named-entities are inherently multilingual, and annotations in any given language may be limited. This motivates us to consider polyglot named-entity recognition (NER), where one model is trained using annotated data drawn from more than one language. However, a straightforward implementation of this simple idea does not always work in practice: naive training of NER models using annotated data drawn from multiple languages consistently underperforms models trained on monolingual data alone, despite having access to more training data. The starting point of this paper is a simple solution to this problem, in which polyglot models are fine-tuned on monolingual data to consistently and significantly outperform their monolingual counterparts. To explain this phenomena, we explore the sources of multilingual transfer in polyglot NER models and examine the weight structure of polyglot models compared to their monolingual counterparts. We find that polyglot models efficiently share many parameters across languages and that fine-tuning may utilize a large number of those parameters.
