The paper describes a transformer-based system designed for SemEval-2023 Task
9: Multilingual Tweet Intimacy Analysis. The purpose of the task was to predict
the intimacy of tweets in a range from 1 (not intimate at all) to 5 (very
intimate). The official training set for the competition consisted of tweets in
six languages (English, Spanish, Italian, Portuguese, French, and Chinese). The
test set included the given six languages as well as external data with four
languages not presented in the training set (Hindi, Arabic, Dutch, and Korean).
We presented a solution based on an ensemble of XLM-T, a multilingual RoBERTa
model adapted to the Twitter domain. To improve the performance of unseen
languages, each tweet was supplemented by its English translation. We explored
the effectiveness of translated data for the languages seen in fine-tuning
compared to unseen languages and estimated strategies for using translated data
in transformer-based models. Our solution ranked 4th on the leaderboard while
achieving an overall Pearson's r of 0.599 over the test set. The proposed
system improves up to 0.088 Pearson's r over a score averaged across all 45
submissions.

本文基于 XLM-T 提出了一种基于 transformer 的系统，用于预测多语言推特的亲密度，使用英语翻译数据可用于优化训练和推理，取得了 0.599 的总体 Pearson's r 值和排名第四的成绩。

使用 XLM-T、Google Translate 和集成学习的多语言推文亲密度检测在 SemEval-2023 任务 9 中的应用

tmn at SemEval-2023 Task 9: Multilingual Tweet Intimacy Detection using  XLM-T, Google Translate, and Ensemble Learning

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

跨语言信息亲密度的多语言 Tweet 分析，通过融合预训练模型与少量合成数据，提出了一种跨语言迁移学习方法 HeFiT，进一步探究其对模型域适应和零样本学习的影响，显著提高了模型性能，证实了合成数据在零样本学习中的作用。