In multilingual translation research, the comprehension and utilization of
language families are of paramount importance. Nevertheless, clustering
languages based solely on their ancestral families can yield suboptimal results
due to variations in the datasets employed during the model's training phase.
To mitigate this challenge, we introduce an innovative method that leverages
the fisher information matrix (FIM) to cluster language families, anchored on
the multilingual translation model's characteristics. We hypothesize that
language pairs with similar effects on model parameters exhibit a considerable
degree of linguistic congruence and should thus be grouped cohesively. This
concept has led us to define pseudo language families. We provide an in-depth
discussion regarding the inception and application of these pseudo language
families. Empirical evaluations reveal that employing these pseudo language
families enhances performance over conventional language families in adapting a
multilingual translation model to unfamiliar language pairs. The proposed
methodology may also be extended to scenarios requiring language similarity
measurements. The source code and associated scripts can be accessed at
this https URL

通过使用费舍尔信息矩阵（FIM）将语言家族进行聚类，我们提出了一种创新的方法来解决多语言翻译模型训练阶段数据集变化所带来的挑战，并定义了伪语言家族。实证评估结果显示，采用这些伪语言家族能够在适应陌生语言对的多语言翻译模型中提高性能，该方法也可扩展到对语言相似度进行测量的场景。