Abstract
We present the results of Shared Task at Workshop DialDoc 2021 that is focused on document-grounded dialogue and conversational question answering. The primary goal of this Shared Task is to build goal-oriented information-seeking conversation systems that can identify the most relevant knowledge in the associated document for generating agent responses in natural language. It includes two subtasks on predicting agent responses: the first subtask is to predict the grounding text span in the given document for next agent response; the second subtask is to generate agent response in natural language given the context. Many submissions outperform baseline significantly. For the first task, the best-performing system achieved 67.1 Exact Match and 76.3 F1. For the second subtask, the best system achieved 41.1 SacreBLEU and highest rank by human evaluation.- Anthology ID:
- 2021.dialdoc-1.1
- 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:
- 1–7
- Language:
- URL:
- https://aclanthology.org/2021.dialdoc-1.1
- DOI:
- 10.18653/v1/2021.dialdoc-1.1
- Cite (ACL):
- Song Feng. 2021. DialDoc 2021 Shared Task: Goal-Oriented Document-grounded Dialogue Modeling. In Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021), pages 1–7, Online. Association for Computational Linguistics.
- Cite (Informal):
- DialDoc 2021 Shared Task: Goal-Oriented Document-grounded Dialogue Modeling (Feng, dialdoc 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.dialdoc-1.1.pdf
- Data
- CoQA, Doc2Dial, MRQA, doc2dial