Concept-to-text Natural Language Generation is the task of expressing an
input meaning representation in natural language. Previous approaches in this
task have been able to generalise to rare or unseen instances by relying on a
delexicalisation of the input. However, this often requires that the input
appears verbatim in the output text. This poses challenges in multilingual
settings, where the task expands to generate the output text in multiple
languages given the same input. In this paper, we explore the application of
multilingual models in concept-to-text and propose Language Agnostic
Delexicalisation, a novel delexicalisation method that uses multilingual
pretrained embeddings, and employs a character-level post-editing model to
inflect words in their correct form during relexicalisation. Our experiments
across five datasets and five languages show that multilingual models
outperform monolingual models in concept-to-text and that our framework
outperforms previous approaches, especially for low resource languages.

本文研究了跨语言多领域自然语言生成的挑战，提出了一种新的基于预训练多语言嵌入的去词法化方法，并设计了字符级后编辑模型来确保词语在重构时的正确形式，结果表明该方法在低资源语言上具有非常好的效果。