Evaluating and explaining training strategies for zero-shot cross-lingual news sentiment analysis
<|reference_start|>Evaluating and explaining training strategies for zero-shot cross-lingual news sentiment analysis: 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.<|reference_end|>
@article{andrenšek2024evaluating,
    title={Evaluating and explaining training strategies for zero-shot
  cross-lingual news sentiment analysis},
    author={Luka Andrenv{s}ek and Boshko Koloski and Andrav{z} Pelicon and Nada
  Lavrav{c} and Senja Pollak and Matthew Purver},
    journal={arXiv preprint arXiv:2409.20054},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.20054},
    primaryClass={cs.CL cs.AI}
}