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.
翻译:编码混合文本数据包括来自一种以上语言的文字或短语的句子。全世界大多数多语言社区使用多种语言交流,通常使用英语。Hinglish是一个由印地语和英语组成的编码混合文本,但以罗马文字写成。本文旨在确定影响由该系统产生的编码混合文本数据质量的因素。对于HinglishEval的任务,拟议的模型使用多种语言的BERT来寻找合成生成的和人为生成的句子之间的相似性,以预测合成生成的辛吉什语句的质量。