A Semantically Conditioned Code-Mixed Natural Language Generation for Task-Oriented Dialog

ne of the most challenging problems in the area of Natural Language Processing and Artificial Intelligence is automatically generating coherent and understandable language for humans, also known as Natural Language Generation (NLG). It is also a crucial component in task-oriented dialog (TOD) systems. In TOD systems, the NLG module converts a dialog state represented in a semantic form into a natural language response. In manystudies, deep learning-based models such as Convolution Neural Networks (CNNs), Long Short Term MemoryNetworks (LSTMs), and encoder-decoder transformer architectures were widely used to map the dialog state to natural language. Most ofthe research NLG was focused on the monolingual data, with amajority of the corpus in the English language. However, in several multilingual regions of the world, such as India, it is natural for speakers to produce utterances and responses which are Code-Mixed. So NLG systems must be trained to deliver a code-mixed multilingual output. In our work, we propose a semantically conditioned - IndicBART (SC-IndicBART) for code-mixed languages and evaluate it using the existing SOTA NLG models.