PreCogIIITH at HinglishEval : Leveraging Code-Mixing Metrics & Language Model Embeddings To Estimate Code-Mix Quality
Artificial Intelligence
2022-06-17 v1
Abstract
Code-Mixing is a phenomenon of mixing two or more languages in a speech event and is prevalent in multilingual societies. Given the low-resource nature of Code-Mixing, machine generation of code-mixed text is a prevalent approach for data augmentation. However, evaluating the quality of such machine generated code-mixed text is an open problem. In our submission to HinglishEval, a shared-task collocated with INLG2022, we attempt to build models factors that impact the quality of synthetically generated code-mix text by predicting ratings for code-mix quality.
Cite
@article{arxiv.2206.07988,
title = {PreCogIIITH at HinglishEval : Leveraging Code-Mixing Metrics & Language Model Embeddings To Estimate Code-Mix Quality},
author = {Prashant Kodali and Tanmay Sachan and Akshay Goindani and Anmol Goel and Naman Ahuja and Manish Shrivastava and Ponnurangam Kumaraguru},
journal= {arXiv preprint arXiv:2206.07988},
year = {2022}
}