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

Unstructured Knowledge Access in Task-oriented Dialog Modeling using  Language Inference, Knowledge Retrieval and Knowledge-Integrative Response  Generation