Papers by Aditya Vavre

2 papers
Training Data Augmentation for Code-Mixed Translation (2021.naacl-main)

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Challenge: We show a 5.8 point increase in BLEU on heavily code-mixed sentences . code-mixing is becoming more commonplace in several bilingual communities .
Approach: They propose a method to convert existing parallel data sources into code-mixed parallel data.
Outcome: The proposed method shows a 5.8 point increase in BLEU on heavily code-mixed sentences on a Hindi-English code-mixed translation task.
Adapting Multilingual Models for Code-Mixed Translation (2022.findings-emnlp)

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Challenge: Prior work has addressed the lack of gold standard code-mixed to pure language parallel data with data augmentation techniques.
Approach: They propose a back-translation-based training scheme for code-mixed translation which eliminates dependence on external resources.
Outcome: The proposed model beats previous work by up to +3.8 BLEU on code-mixed tasks.

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