In the 1960s, an unprecedented computer program called Eliza attempted to simulate the experience of speaking to a therapist. In one exchange, captured in a research paper at the time, a person revealed that her boyfriend had described her as "depressed much of the time." Eliza's response: "I am sorry to hear you are depressed." Eliza, which is widely characterized as the first chatbot, wasn't as versatile as similar services today. The program, which relied on natural language understanding, reacted to key words and then essentially punted the dialogue back to the user.

Attention-based encoder-decoder neural network models have recently shown promising results in goal-oriented dialogue systems. However, these models struggle to reason over and incorporate state-full knowledge while preserving their end-to-end text generation functionality. Since such models can greatly benefit from user intent and knowledge graph integration, in this paper we propose an RNN-based end-to-end encoder-decoder architecture which is trained with joint embeddings of the knowledge graph and the corpus as input. The model provides an additional integration of user intent along with text generation, trained with a multi-task learning paradigm along with an additional regularization technique to penalize generating the wrong entity as output. The model further incorporates a Knowledge Graph entity lookup during inference to guarantee the generated output is state-full based on the local knowledge graph provided. We finally evaluated the model using the BLEU score, empirical evaluation depicts that our proposed architecture can aid in the betterment of task-oriented dialogue system`s performance.