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

• URL: http://arxiv.org/abs/2106.03530v2
• Date: Tue, 8 Jun 2021 03:07:02 GMT
• Title: CAiRE in DialDoc21: Data Augmentation for Information-Seeking Dialogue System
• Authors: Etsuko Ishii, Yan Xu, Genta Indra Winata, Zhaojiang Lin, Andrea Madotto, Zihan Liu, Peng Xu, Pascale Fung
• Abstract summary: 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.
• Score: 55.43871578056878
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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