Evaluating and explaining training strategies for zero-shot cross-lingual news sentiment analysis

• URL: http://arxiv.org/abs/2409.20054v1
• Date: Mon, 30 Sep 2024 07:59:41 GMT
• Title: Evaluating and explaining training strategies for zero-shot cross-lingual news sentiment analysis
• Authors: Luka Andrenšek, Boshko Koloski, Andraž Pelicon, Nada Lavrač, Senja Pollak, Matthew Purver,
• Abstract summary: We introduce novel evaluation datasets in several less-resourced languages. We experiment with a range of approaches including the use of machine translation. We show that language similarity is not in itself sufficient for predicting the success of cross-lingual transfer.
• Score: 8.770572911942635
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate zero-shot cross-lingual news sentiment detection, aiming to develop robust sentiment classifiers that can be deployed across multiple languages without target-language training data. We introduce novel evaluation datasets in several less-resourced languages, and experiment with a range of approaches including the use of machine translation; in-context learning with large language models; and various intermediate training regimes including a novel task objective, POA, that leverages paragraph-level information. Our results demonstrate significant improvements over the state of the art, with in-context learning generally giving the best performance, but with the novel POA approach giving a competitive alternative with much lower computational overhead. We also show that language similarity is not in itself sufficient for predicting the success of cross-lingual transfer, but that similarity in semantic content and structure can be equally important.

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