This paper describes our submission to the SemEval 2023 multilingual tweet
intimacy analysis shared task. The goal of the task was to assess the level of
intimacy of Twitter posts in ten languages. The proposed approach consists of
several steps. First, we perform in-domain pre-training to create a language
model adapted to Twitter data. In the next step, we train an ensemble of
regression models to expand the training set with pseudo-labeled examples. The
extended dataset is used to train the final solution. Our method was ranked
first in five out of ten language subtasks, obtaining the highest average score
across all languages.

本文介绍了在 SemEval 2023 多语言推文亲密度分析共享任务中的提交。任务的目标是评估十种语言的 Twitter 帖子的亲密程度。所提出的方法由几个步骤组成，包括域内预训练、训练回归模型的集成以及使用伪标记示例扩展训练集等。该方法在十个语言子任务中有五个排名第一，在所有语言中获得了最高平均分数。

2023 年 SemEval 任务 9 中的 OPI：一种简单而有效的多语言推文亲密度分析方法

OPI at SemEval 2023 Task 9: A Simple But Effective Approach to  Multilingual Tweet Intimacy Analysis

Intimacy is a fundamental aspect of how we relate to others in social
settings. Language encodes the social information of intimacy through both
topics and other more subtle cues (such as linguistic hedging and swearing).
Here, we introduce a new computational framework for studying expressions of
the intimacy in language with an accompanying dataset and deep learning model
for accurately predicting the intimacy level of questions (Pearson's r=0.87).
Through analyzing a dataset of 80.5M questions across social media, books, and
films, we show that individuals employ interpersonal pragmatic moves in their
language to align their intimacy with social settings. Then, in three studies,
we further demonstrate how individuals modulate their intimacy to match social
norms around gender, social distance, and audience, each validating key
findings from studies in social psychology. Our work demonstrates that intimacy
is a pervasive and impactful social dimension of language.

本研究使用一种新的计算框架研究了人们在语言中表达亲密度的方式，并开发了相应的数据集和深度学习模型，用于准确预测问题的亲密水平，并通过三个研究证明了这种表达方式与社会规范有关。