PESTO: Switching Point based Dynamic and Relative Positional Encoding for Code-Mixed Languages

PESTO: Switching Point based Dynamic and Relative Positional Encoding for Code-Mixed Languages

Abstract

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 building 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 be an effective way to train position sensitive language models for CM text.

PESTO Architecture

Switch Point Attention

If you find this useful, please cite our paper below:

@inproceedings{ali-etal-relative,
title = {PESTO: Switching Point based Dynamic and Relative Positional Encoding for Code-Mixed Languages},
author = {Mohsin Ali and Kandukuri Sai Teja and Sumanth Manduru and Parth Patwa and Amitava Das}
booktitle =  {Proceedings of the AAAI Conference on Artificial Intelligence},
year = {2022},}