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Task-oriented conversational modeling with unstructured knowledge access, astrack 1 of the 9th Dialogue System Technology Challenges (DSTC 9), requests tobuild a system to generate response given dialogue history and knowledgeaccess. This challenge can be separated into three subtasks, (1)knowledge-seeking turn detection, (2) knowledge selection, and (3)knowledge-grounded response generation. We use pre-trained language models,ELECTRA and RoBERTa, as our base encoder for different subtasks. For subtask 1and 2, the coarse-grained information like domain and entity are used toenhance knowledge usage. For subtask 3, we use a latent variable to encodedialog history and selected knowledge better and generate responses combinedwith copy mechanism. Meanwhile, some useful post-processing strategies areperformed on the model's final output to make further knowledge usage in thegeneration task. As shown in released evaluation results, our proposed systemranks second under objective metrics and ranks fourth under human metrics.

Learning to Retrieve Entity-Aware Knowledge and Generate Responses with Copy Mechanism for Task-Oriented Dialogue Systems