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arXiv · 2206.07988

PreCogIIITH at HinglishEval : Leveraging Code-Mixing Metrics & Language Model Embeddings To Estimate Code-Mix Quality

Prashant Kodali·Tanmay Sachan·Akshay Goindani·Anmol Goel·Naman Ahuja·Manish Shrivastava·Ponnurangam Kumaraguru
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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.

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Prashant Kodali, Tanmay Sachan, Akshay Goindani, Anmol Goel, Naman Ahuja, Manish Shrivastava, Ponnurangam Kumaraguru. 2022-06-16. PreCogIIITH at HinglishEval : Leveraging Code-Mixing Metrics & Language Model Embeddings To Estimate Code-Mix Quality. https://arxiv.org/abs/2206.07988

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