In cross-lingual text classification, it is required that task-specific
training data in high-resource source languages are available, where the task
is identical to that of a low-resource target language. However, collecting
such training data can be infeasible because of the labeling cost, task
characteristics, and privacy concerns. This paper proposes an alternative
solution that uses only task-independent word embeddings of high-resource
languages and bilingual dictionaries. First, we construct a dictionary-based
heterogeneous graph (DHG) from bilingual dictionaries. This opens the
possibility to use graph neural networks for cross-lingual transfer. The
remaining challenge is the heterogeneity of DHG because multiple languages are
considered. To address this challenge, we propose dictionary-based
heterogeneous graph neural network (DHGNet) that effectively handles the
heterogeneity of DHG by two-step aggregations, which are word-level and
language-level aggregations. Experimental results demonstrate that our method
outperforms pretrained models even though it does not access to large corpora.
Furthermore, it can perform well even though dictionaries contain many
incorrect translations. Its robustness allows the usage of a wider range of
dictionaries such as an automatically constructed dictionary and crowdsourced
dictionary, which are convenient for real-world applications.

本文提出了一种使用高资源语言的任务无关单词嵌入和双语词典来完成跨语言分类任务的方法，具体实现为通过构建基于词典的异构图和引入两级聚合的 DHGNet 这一图神经网络模型。实验结果表明，该方法在准确度方面优于预训练模型，且对于翻译错误具有一定的鲁棒性，可以扩展到其他自动或人工构建的词典。