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

MCML Authors

Katharina Hämmerl

→ Group Alexander Fraser
 Data Analytics & Statistics

Alina Fastowski

→ Group Gjergji Kasneci
 Responsible Data Science

Alexander Fraser

Prof. Dr.

Principal Investigator

Data Analytics & Statistics

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.

inproceedings

---

Findings @ACL 2023

Findings of the 61th Annual Meeting of the Association for Computational Linguistics. Toronto, Canada, Jul 09-14, 2023.

---

Authors

K. Hämmerl • A. Fastowski • J. Libovický • A. Fraser

---

Links

DOI

---

Research Areas

 A1 | Statistical Foundations & Explainability

 B2 | Natural Language Processing