| Autor: |
Kodali, Prashant, Sachan, Tanmay, Goindani, Akshay, Goel, Anmol, Ahuja, Naman, Shrivastava, Manish, Kumaraguru, Ponnurangam |
| Rok vydání: |
2022 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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