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Cross-Lingual Classification of Political Texts Using Multilingual Sentence Embeddings

Published online by Cambridge University Press:  26 January 2023

Hauke Licht Open the ORCID record for Hauke Licht [Opens in a new window]
Hauke Licht*
Affiliation:
Cologne Center for Comparative Politics, Institute of Political Science and European Affairs, University of Cologne, Cologne, Germany. E-mail: hauke.licht@wiso.uni-koeln.de
*
Corresponding author Hauke Licht
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Abstract

Established approaches to analyze multilingual text corpora require either a duplication of analysts’ efforts or high-quality machine translation (MT). In this paper, I argue that multilingual sentence embedding (MSE) is an attractive alternative approach to language-independent text representation. To support this argument, I evaluate MSE for cross-lingual supervised text classification. Specifically, I assess how reliably MSE-based classifiers detect manifesto sentences’ topics and positions compared to classifiers trained using bag-of-words representations of machine-translated texts, and how this depends on the amount of training data. These analyses show that when training data are relatively scarce (e.g., 20K or less-labeled sentences), MSE-based classifiers can be more reliable and are at least no less reliable than their MT-based counterparts. Furthermore, I examine how reliable MSE-based classifiers label sentences written in languages not in the training data, focusing on the task of discriminating sentences that discuss the issue of immigration from those that do not. This analysis shows that compared to the within-language classification benchmark, such “cross-lingual transfer” tends to result in fewer reliability losses when relying on the MSE instead of the MT approach. This study thus presents an important addition to the cross-lingual text analysis toolkit.

Keywords

multilingual embeddingmultilingual text analysissupervised machine learning

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Type
Article
Information
Political Analysis , Volume 31 , Issue 3 , July 2023 , pp. 366 - 379
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of the Society for Political Methodology

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Footnotes

Edited by Jeff Gill

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