An Empirical Study on Crosslingual Transfer in Probabilistic Topic Models

TL;DR

This paper systematically investigates how different multilingual probabilistic topic models transfer knowledge across languages, providing empirical insights to guide their application and future development.

Contribution

It offers a comprehensive empirical analysis of various multilingual topic models under diverse training conditions, highlighting their transfer mechanisms and performance.

Findings

Different models exhibit varied transfer capabilities.

Training conditions significantly impact model performance.

Insights inform better model selection and development.

Abstract

Probabilistic topic modeling is a popular choice as the first step of crosslingual tasks to enable knowledge transfer and extract multilingual features. While many multilingual topic models have been developed, their assumptions on the training corpus are quite varied, and it is not clear how well the models can be applied under various training conditions. In this paper, we systematically study the knowledge transfer mechanisms behind different multilingual topic models, and through a broad set of experiments with four models on ten languages, we provide empirical insights that can inform the selection and future development of multilingual topic models.