  Persona and Knowledge dual context open-domain chat is a novel dialogue
generation task introduced recently. While Persona and Knowledge is each
interesting context of open-domain dialogue, the combination of both has not
been well studied. We tackle Persona-Knowledge identification and response
generation tasks in this paper. We design an informed data augmentation
strategy that is compatible with neural Q&A retrieval models. With the
augmented data, we perform permutative Persona-Knowledge evaluation and
successive Persona search fine-tuning. Furthermore, we perform dialogue
generation with various decoding techniques and illustrate crucial elements. We
achieve SOTA across official metrics with 93.99% Grounding accuracy average and
23.62 SacreBLEU score.
