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    <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.</abstract>
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%0 Conference Proceedings
%T DialDoc 2021 Shared Task: Goal-Oriented Document-grounded Dialogue Modeling
%A Feng, Song
%Y Feng, Song
%Y Reddy, Siva
%Y Alikhani, Malihe
%Y He, He
%Y Ji, Yangfeng
%Y Iyyer, Mohit
%Y Yu, Zhou
%S Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F feng-2021-dialdoc
%X 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.
%R 10.18653/v1/2021.dialdoc-1.1
%U https://aclanthology.org/2021.dialdoc-1.1
%U https://doi.org/10.18653/v1/2021.dialdoc-1.1
%P 1-7

Markdown (Informal)

[DialDoc 2021 Shared Task: Goal-Oriented Document-grounded Dialogue Modeling](https://aclanthology.org/2021.dialdoc-1.1) (Feng, dialdoc 2021)

• DialDoc 2021 Shared Task: Goal-Oriented Document-grounded Dialogue Modeling (Feng, dialdoc 2021)

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.