Abstract
This paper describes an end to end dialog system created using sequence to sequence learning and memory networks for Telugu, a low-resource language. We automatically generate dialog data for Telugu in the tourist domain, using a knowledge base that provides tourist place, type, tour time, etc. Using this data, we train a sequence to sequence model to learn system responses in the dialog. In order to add the query prediction for information retrieval (through API calls), we train a memory network. We also handle cases requiring updation of API calls and querying for additional information. Using the combination of sequence to sequence learning and memory network, we successfully create an end to end dialog system for Telugu.