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Information-seeking dialogue systems, including knowledge identification andresponse generation, aim to respond to users with fluent, coherent, andinformative responses based on users' needs, which. To tackle this challenge,we utilize data augmentation methods and several training techniques with thepre-trained language models to learn a general pattern of the task and thusachieve promising performance. In DialDoc21 competition, our system achieved74.95 F1 score and 60.74 Exact Match score in subtask 1, and 37.72 SacreBLEUscore in subtask 2. Empirical analysis is provided to explain the effectivenessof our approaches.

CAiRE in DialDoc21: Data Augmentation for Information-Seeking Dialogue System