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

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

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

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arxiv 2206.07988 v1 pith:BTREPEON submitted 2022-06-16 cs.AI

Prashant Kodali , Tanmay Sachan , Akshay Goindani , Anmol Goel , Naman Ahuja , Manish Shrivastava , Ponnurangam Kumaraguru This is my paper · ORCID

classification cs.AI

keywords qualitycode-mixcode-mixingtextcode-mixedgeneratedhinglishevalmachine

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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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