WKU _ NLP at S em E val-2023 Task 9: Translation Augmented Multilingual Tweet Intimacy Analysis (2023)

Abstract: This paper describes a system for the SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis. This system consists of a pretrained multilingual masked language model as a text encoder and a neural network as a regression model. Data augmentation based on neural machine translation models is adopted to improve model performance under the low-resource scenario. This system is further improved through the ensemble of multiple models with the best performance in each language. This system ranks 4th in languages unseen in the training data and 16th in languages seen in the training data. The code and data can be found in this link: https://github.com/Cloudy0219/Multilingual .

## introduction intimacy is a fundamental aspect of interpersonal relationships within various social settings. languages inherently encode rich social information pertaining to intimacy, offering opportunities to explore and analyze intimacy through computational linguistics perspectives. assessing intimacy in a language not only provides insights into the complexities of human communication but also serves as a test for computational models' ability to comprehend human social interactions and the underlying social norms related to intimacy. assessing intimacy in a language not only provides insights into the complexities of human communication but also serves as a test for computational models' ability to comprehend human social interactions and the underlying social norms related to intimacy. social media platforms, such as twitter, present a treasure trove of diverse interactions with varying degrees of intimacy, making them an ideal source for investigating linguistic intimacy patterns. @xcite introduced a benchmark to examine intimacy interactions on twitter, comprising english tweets with human-labeled intimacy scores. @xcite further expanded this benchmark to a multilingual setting, covering ten languages, further increasing the scope and relevance of intimacy analysis across diverse linguistic communities. despite its significance, intimacy analysis remains a challenging task due to the limited data available and the inherent difficulties for models to interpret and understand human intimacy. this challenge is further intensified in multilingual contexts, as the expression of intimacy varies across languages and cultures. in response, i propose a novel regression model that leverages recent advancements in language modeling and neural networks to capture the semantics of intimacy in multiple languages effectively. to address the issues of limited resources and language differences, i also incorporate data augmentation and model en-sembling techniques to enhance my model's performance. by bridging the gap between computational linguistics and the multifaceted nature of intimacy, my approach aims to contribute significantly to understanding human social dynamics across diverse linguistic contexts. ## system overview in this section, i present a comprehensive architecture of my system and elaborate on the implementation details of each component. my system comprises several primary modules: (1) multilingual data augmentation module; (2) text encoder; (3) regression model. the system's overall structure is illustrated in figure 1 . the text encoder transforms tokenized input sentences into high-dimensional representations, embedding the semantic information necessary to determine intimacy scores. as described in section 2.2, the regression model will predict intimacy scores based on the information contained within these representations. ## experiment setup this section describes the dataset used for training my system and the machine translation model used for data augmentation. i also discuss the details of data processing, model parameters, and evaluation. ## results model performance is analyzed in three aspects: ## conclusion in this paper, i present a system for multilingual intimacy analysis, comprising a pre-trained multilingual masked language model and a regression model designed to project representations into a single intimacy value. to enhance the model's performance in low-resource settings, i employ translation models for data augmentation, which results in improved performance on languages not encountered during training. in order to select the best-performing model, i employ an ensemble of multiple models, each with optimal performance in their respective languages. this system achieves a rank of 4 th for unseen languages and 16 th for languages present in the training data.