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This paper describes the submission of UZH_CLyp for the SemEval 2023 Task 9"Multilingual Tweet Intimacy Analysis". We achieved second-best results in all10 languages according to the official Pearson's correlation regressionevaluation measure. Our cross-lingual transfer learning approach explores thebenefits of using a Head-First Fine-Tuning method (HeFiT) that first updatesonly the regression head parameters and then also updates the pre-trainedtransformer encoder parameters at a reduced learning rate. Additionally, westudy the impact of using a small set of automatically generated examples (inour case, from ChatGPT) for low-resource settings where no human-labeled datais available. Our study shows that HeFiT stabilizes training and consistentlyimproves results for pre-trained models that lack domain adaptation to tweets.Our study also shows a noticeable performance increase in cross-linguallearning when synthetic data is used, confirming the usefulness of current textgeneration systems to improve zero-shot baseline results. Finally, we examinehow possible inconsistencies in the annotated data contribute to cross-lingualinterference issues.

UZH_CLyp at SemEval-2023 Task 9: Head-First Fine-Tuning and ChatGPT Data Generation for Cross-Lingual Learning in Tweet Intimacy Prediction