Exploring Anisotropy and Outliers in Multilingual Language Models for Cross-Lingual Semantic Sentence Similarity

TL;DR

This paper investigates the anisotropy and outlier dimensions in multilingual language models, exploring how these factors affect cross-lingual semantic similarity and proposing space transformations to improve representation quality.

Contribution

It analyzes outlier dimensions in multilingual models and demonstrates that simple embedding space transformations can enhance isotropy and performance without fine-tuning.

Findings

Transformations like removing outliers improve isotropy.

Fine-tuned models exhibit more isotropic representations.

Space transformations can close performance gaps without additional training.

Abstract

Previous work has shown that the representations output by contextual language models are more anisotropic than static type embeddings, and typically display outlier dimensions. This seems to be true for both monolingual and multilingual models, although much less work has been done on the multilingual context. Why these outliers occur and how they affect the representations is still an active area of research. We investigate outlier dimensions and their relationship to anisotropy in multiple pre-trained multilingual language models. We focus on cross-lingual semantic similarity tasks, as these are natural tasks for evaluating multilingual representations. Specifically, we examine sentence representations. Sentence transformers which are fine-tuned on parallel resources (that are not always available) perform better on this task, and we show that their representations are more isotropic. However, we aim to improve multilingual representations in general. We investigate how much of the performance difference can be made up by only transforming the embedding space without fine-tuning, and visualise the resulting spaces. We test different operations: Removing individual outlier dimensions, cluster-based isotropy enhancement, and ZCA whitening. We publish our code for reproducibility.