Exploring Dimensionality Reduction Techniques in Multilingual Transformers

TL;DR

This paper investigates how various dimensionality reduction techniques affect the performance of multilingual Siamese Transformers on semantic tasks, demonstrating significant dimension reduction with minimal performance loss.

Contribution

It provides a comprehensive analysis of multiple dimensional reduction methods on multilingual transformers, highlighting their impact on semantic task performance and visualization.

Findings

Achieved up to 91.58% dimension reduction with minimal performance degradation.

Dimensionality reduction techniques can effectively simplify high-dimensional embeddings.

Results inform better model tuning and visualization strategies for NLP tasks.

Abstract

Both in scientific literature and in industry,, Semantic and context-aware Natural Language Processing-based solutions have been gaining importance in recent years. The possibilities and performance shown by these models when dealing with complex Language Understanding tasks is unquestionable, from conversational agents to the fight against disinformation in social networks. In addition, considerable attention is also being paid to developing multilingual models to tackle the language bottleneck. The growing need to provide more complex models implementing all these features has been accompanied by an increase in their size, without being conservative in the number of dimensions required. This paper aims to give a comprehensive account of the impact of a wide variety of dimensional reduction techniques on the performance of different state-of-the-art multilingual Siamese Transformers, including unsupervised dimensional reduction techniques such as linear and nonlinear feature extraction, feature selection, and manifold techniques. In order to evaluate the effects of these techniques, we considered the multilingual extended version of Semantic Textual Similarity Benchmark (mSTSb) and two different baseline approaches, one using the pre-trained version of several models and another using their fine-tuned STS version. The results evidence that it is possible to achieve an average reduction in the number of dimensions of $91.58\% \pm 2.59\%$ and $54.65\% \pm 32.20\%$, respectively. This work has also considered the consequences of dimensionality reduction for visualization purposes. The results of this study will significantly contribute to the understanding of how different tuning approaches affect performance on semantic-aware tasks and how dimensional reduction techniques deal with the high-dimensional embeddings computed for the STS task and their potential for highly demanding NLP tasks