  The Track-1 of DSTC9 aims to effectively answer user requests or questions
during task-oriented dialogues, which are out of the scope of APIs/DB. By
leveraging external knowledge resources, relevant information can be retrieved
and encoded into the response generation for these out-of-API-coverage queries.
In this work, we have explored several advanced techniques to enhance the
utilization of external knowledge and boost the quality of response generation,
including schema guided knowledge decision, negatives enhanced knowledge
selection, and knowledge grounded response generation. To evaluate the
performance of our proposed method, comprehensive experiments have been carried
out on the publicly available dataset. Our approach was ranked as the best in
human evaluation of DSTC9 Track-1.
