ZBL 2 W at S em E val-2023 Task 9: A Multilingual Fine-tuning Model with Data Augmentation for Tweet Intimacy Analysis (2023)

Abstract: This paper describes our system used in the SemEval-2023 Task 9 Multilingual Tweet Intimacy Analysis. There are two key challenges in this task: the complexity of multilingual and zero-shot cross-lingual learning, and the difficulty of semantic mining of tweet intimacy. To solve the above problems, our system extracts contextual representations from the pretrained language models, XLM-T, and employs various optimization methods, including adversarial training, data augmentation, ordinal regression loss and special training strategy. Our system ranked 14th out of 54 participating teams on the leaderboard and ranked 10th on predicting languages not in the training data. Our code is available on Github.

## introduction a theoretical approach to understanding the level of intimacy, human emotion, and social connections in communication is called intimacy in language. quantifying the intimacy expressed in language plays a key role in revealing important social norms in various context @xcite . semeval-2023 task 9 aims at mining intimacy in tweets. @xcite provides a new multilingual intimacy analysis dataset covering 13,372 tweets in 10 languages. text intimacy is labeled on a continuum scale, in the range of @xcite . among them, the participants are only given the training set in six languages (english, spanish, italian, portuguese, french, and chinese), and the model performance will be evaluated on the test set of ten languages, including 4 languages not in the training set @xcite . given that the semeval-2023 task 9 is a complex challenge that integrates language transfer and * these authors contributed equally to this work. ## related work intimacy computing is an emerging field of natural language processing. @xcite propose to encode social messages of intimacy through topics and other more subtle cues such as swear words. a new computational framework for studying the expression of intimacy in language is introduced, accompanied by multilingual datasets, mint, and deep learning models. with the development of pre-training technology, multi-lingual pre-train model based methods are becoming the new trend to solve cross-lingual tasks. conneau and lample (2019) extend generative pre-training to multiple languages and show the effectiveness of cross-lingual pre-training for the first time. knowledge distillation methods @xcite have been widely used in crosslanguage model research, but distillbert @xcite and minilm @xcite do not perform well for intimacy tasks. our work builds on the work of @xcite , which takes into account the unique expression of tweets and the generalization of multiple languages. ## conclusion and future work in this paper, we present an intimacy analysis system by deploying various optimization methods, including adversarial training, data augmentation, ordinal regression loss and special training methods, to semeval-2023 task 9. compared with the method of sharing the same task in multiple languages, building a multi-task model for different languages is a more worthy of analysis. multilingual research based on pretraining is more dependent on the diversity of languages contained in the pre-training corpus. for low-resource languages, the performance of the model will get stuck in a bottleneck. in the future, we will further explore the relationships between different linguistic features and build a stronger multilingual multitasking system for the intimacy analysis.