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The mixing of two or more languages is called Code-Mixing (CM). CM is asocial norm in multilingual societies. Neural Language Models (NLMs) liketransformers have been effective on many NLP tasks. However, NLM for CM is anunder-explored area. Though transformers are capable and powerful, they cannotalways encode positional information since they are non-recurrent. Therefore,to enrich word information and incorporate positional information, positionalencoding is defined. We hypothesize that Switching Points (SPs), i.e.,junctions in the text where the language switches (L1 -> L2 or L2 -> L1), posea challenge for CM Language Models (LMs), and hence give special emphasis toSPs in the modeling process. We experiment with several positional encodingmechanisms and show that rotatory positional encodings along with switchingpoint information yield the best results.  We introduce CONFLATOR: a neural language modeling approach for code-mixedlanguages. CONFLATOR tries to learn to emphasize switching points using smarterpositional encoding, both at unigram and bigram levels. CONFLATOR outperformsthe state-of-the-art on two tasks based on code-mixed Hindi and English(Hinglish): (i) sentiment analysis and (ii) machine translation.

CONFLATOR: Incorporating Switching Point based Rotatory Positional Encodings for Code-Mixed Language Modeling