Shuang Cheng


2023

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Enhancing Multilingual Document-Grounded Dialogue Using Cascaded Prompt-Based Post-Training Models
Jun Liu | Shuang Cheng | Zineng Zhou | Yang Gu | Jian Ye | Haiyong Luo
Proceedings of the Third DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering

The Dialdoc23 shared task presents a Multilingual Document-Grounded Dialogue Systems (MDGDS) challenge, where system responses are generated in multiple languages using user’s queries, historical dialogue records and relevant passages. A major challenge for this task is the limited training data available in low-resource languages such as French and Vietnamese. In this paper, we propose Cascaded Prompt-based Post-training Models, dividing the task into three subtasks: Retrieval, Reranking and Generation. We conduct post-training on high-resource language such as English and Chinese to enhance performance of low-resource languages by using the similarities of languages. Additionally, we utilize the prompt method to activate model’s ability on diverse languages within the dialogue domain and explore which prompt is a good prompt. Our comprehensive experiments demonstrate the effectiveness of our proposed methods, which achieved the first place on the leaderboard with a total score of 215.40 in token-level F1, SacreBleu, and Rouge-L metrics.