Code-switching is a prevalent linguistic phenomenon in which multilingual individuals seamlessly alternate between languages. Despite its widespread use online and recent research trends in this area, research in code-switching presents unique challenges, primarily stemming from the scarcity of labelled data and available resources. In this study we investigate how pre-trained Language Models handle code-switched text in three dimensions: a) the ability of PLMs to detect code-switched text, b) variations in the structural information that PLMs utilise to capture code-switched text, and c) the consistency of semantic information representation in code-switched text. To conduct a systematic and controlled evaluation of the language models in question, we create a novel dataset of well-formed naturalistic code-switched text along with parallel translations into the source languages. Our findings reveal that pre-trained language models are effective in generalising to code-switched text, shedding light on the abilities of these models to generalise representations to CS corpora. We release all our code and data including the novel corpus at https://github.com/francesita/code-mixed-probes.
Can Large Language Models Understand, Reason About, and Generate Code-Switched Text?
Capital One（第一资本）；Stanford University（斯坦福大学）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；KAIST（韩国科学技术院）；Brown University（布朗大学）；NAIST（奈良先端科学技术大学院大学）；UC Berkeley（加州大学伯克利分校）；SAS Posos；Oracle（甲骨文公司）；ITB（万隆理工学院）；The Hong Kong Polytechnic University（香港理工大学）
CS-Embed at SemEval-2020 Task 9: The effectiveness of code-switched word embeddings for sentiment analysis
University of Birmingham（伯明翰大学）；Birmingham City University（伯明翰城市大学）
社交媒体帖子的情感分析的日益普及和应用自然导致了对以多种语言撰写的帖子的情感分析，这种做法被称为代码切换。虽然最近对代码转换帖子的研究集中在使用多语言词嵌入上，但这些嵌入并没有在代码转换数据上进行训练。在这项工作中，我们提出了在代码切换推文上训练的词嵌入，特别是那些使用西班牙语和英语的推文，称为西班牙式英语。我们探索嵌入空间，以发现它们如何捕捉两种语言中单词的含义。我们通过参加SemEval 2020任务9：~ \emph{Sentiment Analysis on Code-Mixed Social Media Text}来测试这些嵌入的有效性。我们利用它们来训练情感分类器，其F-1得分为0.722。这高于0.656的比赛基线，我们的团队（codalab用户名\emph{francesita}）在29个参赛团队中排名第14，击败了基线。
MiLQ: Benchmarking IR Models for Bilingual Web Search with Mixed Language Queries
Graduate School of Artificial Intelligence, POSTECH, Republic of Korea（POSTECH人工智能研究生院）；Department of Computer Science and Engineering, POSTECH, Republic of Korea（POSTECH计算机科学与工程系）；Lilt, Inc.（Lilt公司）
本文提出首个真实且较受偏好的混合语言查询公开基准 MiLQ，评估多语言 IR 模型并揭示其表现不一致，同时验证英语混用可通过增强 token 匹配改善英文文档检索。
AtteSTNet -- An attention and subword tokenization based approach for code-switched text hate speech detection
Department of Computer Science（计算机科学系）；University of Southern California（南加州大学）；Fu Foundation School of Engineering and Applied Science（工程与应用科学基金会学院）；Columbia University（哥伦比亚大学）
DECT: Harnessing LLM-assisted Fine-Grained Linguistic Knowledge and Label-Switched and Label-Preserved Data Generation for Diagnosis of Alzheimer's Disease
The University of Hong Kong（香港大学）；The Chinese University of Hong Kong（香港中文大学）
