BITS Pilani at HinglishEval: Quality Evaluation for Code-Mixed Hinglish Text Using Transformers
This addresses the need for quality assessment in code-mixed text generation for multi-lingual communities, but it is incremental as it applies an existing method to a specific domain.
The paper tackled the problem of evaluating the quality of synthetically generated Hinglish code-mixed text by using multi-lingual BERT to measure similarity between synthetic and human-generated sentences, achieving results on the HinglishEval task.
Code-Mixed text data consists of sentences having words or phrases from more than one language. Most multi-lingual communities worldwide communicate using multiple languages, with English usually one of them. Hinglish is a Code-Mixed text composed of Hindi and English but written in Roman script. This paper aims to determine the factors influencing the quality of Code-Mixed text data generated by the system. For the HinglishEval task, the proposed model uses multi-lingual BERT to find the similarity between synthetically generated and human-generated sentences to predict the quality of synthetically generated Hinglish sentences.