A major challenge in analysing social me-dia data belonging to languages that use non-English script is its code-mixed nature. Recentresearch has presented state-of-the-art contex-tual embedding models (both monolingual s.a.BERT and multilingual s.a.
XLM-R) as apromising approach. In this paper, we showthat the performance of such embedding mod-els depends on multiple factors, such as thelevel of code-mixing in the dataset, and thesize of the training dataset. We empiricallyshow that a newly introduced Capsule+biGRUclassifier could outperform a classifier built onthe English-BERT as well as XLM-R just witha training dataset of about 6500 samples forthe Sinhala-English code-mixed data.
Code-mixing has become a moving method of communication among multilingual speakers. Most of the social media content of the multilingual societies are written in code-mixed text. However, most of the current translation systems neglect to convert co
de-mixed texts to a standard language. Most of the user written code-mixed content in social media remains unprocessed due to the unavailability of linguistic resource such as parallel corpus. This paper proposes a Neural Machine Translation(NMT) model to translate the Sinhala-English code-mixed text to the Sinhala language. Due to the limited resources available for Sinhala-English code-mixed(SECM) text, a parallel corpus is created with SECM sentences and Sinhala sentences. Srilankan social media sites contain SECM texts more frequently than the standard languages. The model proposed for code-mixed text translation in this study is a combination of Encoder-Decoder framework with LSTM units and Teachers Forcing Algorithm. The translated sentences from the model are evaluated using BLEU(Bilingual Evaluation Understudy) metric. Our model achieved a remarkable BLEU score for the translation.
