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.
@article{arxiv.2106.03530,
title = {CAiRE in DialDoc21: Data Augmentation for Information-Seeking Dialogue System},
author = {Etsuko Ishii and Yan Xu and Genta Indra Winata and Zhaojiang Lin and Andrea Madotto and Zihan Liu and Peng Xu and Pascale Fung},
journal= {arXiv preprint arXiv:2106.03530},
year = {2021}
}
Comments
Accepted in DialDoc21 Workshop in ACL 2021. Etsuko Ishii and Yan Xu contributed equally to this work