Code-switching is a common feature of multilingual communication, and identifying where the language switches reliably is essential for downstream tasks such as generating code-switched machine translations. This paper introduces CSDI, a Code-Switching Detection (CSD) system for Indic text, which jointly learns CSD, Named Entity Recognition, and Part-of-Speech tagging through a shared encoder. Leveraging multitask learning, CSDI captures linguistic cues that signal switching boundaries and achieves a new state-of-the-art macro-F1 score with near-zero 𝛥CMI across six Indic languages. The model also demonstrates strong cross-lingual transfer, effectively leveraging high-resource languages to improve low-resource performance. Despite challenges such as intra-word code-mixing and limited token-level context, CSDI establishes a new baseline for scalable, low-resource NLP research in code-mixed environments.

This report describes first an industrial use case for identifying closely related languages, e.g.dialects, namely the detection of languages of movie subtitle documents. We then presenta 2-stage architecture that is able to detect macrolanguages in the first stage and languagevariants in the second. Using our architecture, we participated in the DSL-TL Shared Task of the VarDial 2023 workshop. We describe the results of our experiments. In the first experiment we report an accuracy of 97.8% on a set of 460 subtitle files. In our second experimentwe used DSL-TL data and achieve a macroaverage F1 of 76% for the binary task, and 54% for the three-way task in the dev set. In the open track, we augment the data with named entities retrieved from Wikidata and achieve minor increases of about 1% for both tracks.