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.
CITATION STYLE
Feng, S. (2021). DialDoc 2021 Shared Task: Goal-Oriented Document-grounded Dialogue Modeling. In DialDoc 2021 - 1st Workshop on Document-Grounded Dialogue and Conversational Question Answering, Proceedings (pp. 1–7). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.dialdoc-1.1
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