Representativeness as a Forgotten Lesson for Multilingual and Code-switched Data Collection and Preparation
This work addresses data quality issues for researchers and practitioners building multilingual and code-switched AI systems, but it is incremental as it critiques existing practices without proposing a new method.
The paper investigates why code-switching (CSW) systems have not progressed despite advances in multilingual language models, by analyzing 68 CSW datasets and finding flaws in representativeness due to biases like over-reliance on English and ignoring location, socio-demographic, and register variations.
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.