  NLP applications for code-mixed (CM) or mix-lingual text have gained a
significant momentum recently, the main reason being the prevalence of language
mixing in social media communications in multi-lingual societies like India,
Mexico, Europe, parts of USA etc. Word embeddings are basic build-ing blocks of
any NLP system today, yet, word embedding for CM languages is an unexplored
territory. The major bottleneck for CM word embeddings is switching points,
where the language switches. These locations lack in contextually and
statistical systems fail to model this phenomena due to high variance in the
seen examples. In this paper we present our initial observations on applying
switching point based positional encoding techniques for CM language,
specifically Hinglish (Hindi - English). Results are only marginally better
than SOTA, but it is evident that positional encoding could bean effective way
to train position sensitive language models for CM text.
