  Dialog systems enriched with external knowledge can handle user queries that
are outside the scope of the supporting databases/APIs. In this paper, we
follow the baseline provided in DSTC9 Track 1 and propose three subsystems,
KDEAK, KnowleDgEFactor, and Ens-GPT, which form the pipeline for a
task-oriented dialog system capable of accessing unstructured knowledge.
Specifically, KDEAK performs knowledge-seeking turn detection by formulating
the problem as natural language inference using knowledge from dialogs,
databases and FAQs. KnowleDgEFactor accomplishes the knowledge selection task
by formulating a factorized knowledge/document retrieval problem with three
modules performing domain, entity and knowledge level analyses. Ens-GPT
generates a response by first processing multiple knowledge snippets, followed
by an ensemble algorithm that decides if the response should be solely derived
from a GPT2-XL model, or regenerated in combination with the top-ranking
knowledge snippet. Experimental results demonstrate that the proposed pipeline
system outperforms the baseline and generates high-quality responses, achieving
at least 58.77% improvement on BLEU-4 score.
