  Grounding dialogue on external knowledge and interpreting linguistic patterns
in dialogue history context, such as ellipsis, anaphora, and co-references is
critical for dialogue comprehension and generation. In this paper, we present a
novel open-domain dialogue generation model which effectively utilizes the
large-scale commonsense and named entity based knowledge in addition to the
unstructured topic-specific knowledge associated with each utterance. We
enhance the commonsense knowledge with named entity-aware structures using
co-references. Our proposed model utilizes a multi-hop attention layer to
preserve the most accurate and critical parts of the dialogue history and the
associated knowledge. In addition, we employ a Commonsense and Named Entity
Enhanced Attention Module, which starts with the extracted triples from various
sources and gradually finds the relevant supporting set of triples using
multi-hop attention with the query vector obtained from the interactive
dialogue-knowledge module. Empirical results on two benchmark dataset
demonstrate that our model significantly outperforms the state-of-the-art
methods in terms of both automatic evaluation metrics and human judgment. Our
code is publicly available at
\href{https://github.com/deekshaVarshney/CNTF}{https://github.com/deekshaVarshney/CNTF};
\href{https://www.iitp.ac.in/~ai-nlp-ml/resources/codes/CNTF.zip}{https://www.iitp.ac.in/-ai-nlp-ml/resources/
codes/CNTF.zip}.
