  Information-seeking dialogue systems, including knowledge identification and
response generation, aim to respond to users with fluent, coherent, and
informative responses based on users' needs, which. To tackle this challenge,
we utilize data augmentation methods and several training techniques with the
pre-trained language models to learn a general pattern of the task and thus
achieve promising performance. In DialDoc21 competition, our system achieved
74.95 F1 score and 60.74 Exact Match score in subtask 1, and 37.72 SacreBLEU
score in subtask 2. Empirical analysis is provided to explain the effectiveness
of our approaches.
