代码混合的情感与仇恨言论预测
Code-mixed Sentiment and Hate-speech Prediction
AI总结：
针对代码混合文本的情感分析和仇恨言论检测任务，研究构建了支持非正式语言的英-印地语、英-斯洛文尼亚语双语预训练模型，并评估多类模型性能，发现微调双语及社交媒体专用多语模型表现最佳且在代码混合数据上性能略优。
摘要：
代码混合语篇在单篇文本中结合多种语言，常见于拥有多种官方语言国家的非正式语篇，也存在于其他许多国家与英语或邻国语言的结合使用中。随着大型语言模型主导多数自然语言处理任务，我们研究了其在代码混合场景下相关任务的性能。首先，我们针对英语-印地语和英语-斯洛文尼亚语创建了四个新的双语预训练掩码语言模型，专门用于支持非正式语言。随后，我们使用情感分析和社交媒体文本中的攻击性语言检测这两个频繁包含代码混合文本的任务，对单语、双语、少语和大规模多语模型在多种语言上进行了评估。结果显示，最成功的分类器是针对社交媒体文本优化的微调双语模型和多语模型，其次是非专用大规模多语模型和单语模型，而大型生成式模型不具竞争力；在情感相关任务中，模型在代码混合数据上的表现大多略优于非代码混合数据。
英文摘要：
Code-mixed discourse combines multiple languages in a single text. It is commonly used in informal discourse in countries with several official languages, but also in many other countries in combination with English or neighboring languages. As recently large language models have dominated most natural language processing tasks, we investigated their performance in code-mixed settings for relevant tasks. We first created four new bilingual pre-trained masked language models for English-Hindi and English-Slovene languages, specifically aimed to support informal language. Then we performed an evaluation of monolingual, bilingual, few-lingual, and massively multilingual models on several languages, using two tasks that frequently contain code-mixed text, in particular, sentiment analysis and offensive language detection in social media texts. The results show that the most successful classifiers are fine-tuned bilingual models and multilingual models, specialized for social media texts, followed by non-specialized massively multilingual and monolingual models, while huge generative models are not competitive. For our affective problems, the models mostly perform slightly better on code-mixed data compared to non-code-mixed data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
