Clustering Pseudo Language Family in Multilingual Translation Models
  with Fisher Information Matrix
Xinyu Ma Xuebo Liu Min Zhang
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
https://github.com/ecoli-hit/PseudoFamily.