CAiRE in DialDoc21: Data Augmentation for Information Seeking Dialogue System
Yan Xu, Etsuko Ishii, Genta Indra Winata, Zhaojiang Lin, Andrea Madotto, Zihan Liu, Peng Xu, Pascale Fung
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.- Anthology ID:
- 2021.dialdoc-1.6
- Volume:
- Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venue:
- dialdoc
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 46–51
- Language:
- URL:
- https://aclanthology.org/2021.dialdoc-1.6
- DOI:
- 10.18653/v1/2021.dialdoc-1.6
- Cite (ACL):
- Yan Xu, Etsuko Ishii, Genta Indra Winata, Zhaojiang Lin, Andrea Madotto, Zihan Liu, Peng Xu, and Pascale Fung. 2021. CAiRE in DialDoc21: Data Augmentation for Information Seeking Dialogue System. In Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021), pages 46–51, Online. Association for Computational Linguistics.
- Cite (Informal):
- CAiRE in DialDoc21: Data Augmentation for Information Seeking Dialogue System (Xu et al., dialdoc 2021)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2021.dialdoc-1.6.pdf
- Code
- HLTCHKUST/CAiRE_in_DialDoc21
- Data
- MRQA, SearchQA, TriviaQA