Multilingualism is widespread around the world and code-switching (CSW) is a
common practice among different language pairs/tuples across locations and
regions. However, there is still not much progress in building successful CSW
systems, despite the recent advances in Massive Multilingual Language Models
(MMLMs). We investigate the reasons behind this setback through a critical
study about the existing CSW data sets (68) across language pairs in terms of
the collection and preparation (e.g. transcription and annotation) stages. This
in-depth analysis reveals that \textbf{a)} most CSW data involves English
ignoring other language pairs/tuples \textbf{b)} there are flaws in terms of
representativeness in data collection and preparation stages due to ignoring
the location based, socio-demographic and register variation in CSW. In
addition, lack of clarity on the data selection and filtering stages shadow the
representativeness of CSW data sets. We conclude by providing a short
check-list to improve the representativeness for forthcoming studies involving
CSW data collection and preparation.

研究分析了现有的跨语言切换数据集，揭示了大多数数据涉及英语，忽略了其他语言对 / 元组，并且在数据收集和准备阶段存在代表性方面的缺陷，忽视了跨语言切换中的地理位置、社会人口统计学和注册变化。最后提供了一个简短的清单，以改善与跨语言切换数据收集和准备相关的代表性。