Knowing What to Say: Towards knowledge grounded code-mixed response generation for open-domain conversations

摘要

Inculcating knowledge in the dialogue agents is an important step towards creating any agent more human-like. Hence, the use of knowledge while conversing is crucial for building interactive and engaging systems. Most existing works for developing social conversation systems focus on monolingual discussions, with little research on multilingual or code-mixed conversations. Therefore, in this work, we propose generating knowledge-aware code-mixed responses for building end-to-end code-mixed dialogue systems. We design a reinforced transformer framework that uses task-specific rewards for training the entire system. In addition, we utilize a knowledge selection module that captures the appropriate knowledge and generates responses using a deliberation decoder. We introduce a Knowledge aware Code-Mixed (KCM) dataset that consists of conversations grounded in knowledge for four Indian languages (Hindi, Bengali, Gujarati, and Telugu) and two European languages (Spanish and French). Quantitative and qualitative analysis show that the proposed framework on the newly created KCM dataset performs superior to the existing baselines for all the metrics.