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    <abstract>The paper presents the results of the Shared Task hosted by the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering co-located at ACL 2022. The primary goal of this Shared Task is to build goal-oriented information-seeking conversation systems that are grounded in the domain documents, where each dialogue could correspond to multiple subtasks that are based on different documents. The task is to generate agent responses in natural language given the dialogue and document contexts. There are two task settings and leaderboards based on (1) the same sets of domains (SEEN) and (2) one unseen domain (UNSEEN). There are over 20 teams participating in Dev Phase and 8 teams participating in both Dev and Test Phases. Multiple submissions significantly outperform the baseline. The best-performing system achieves 52.06 F1 and the total of 191.30 on the SEEN task; and 34.65 F1 and the total of 130.79 on the UNSEEN task.</abstract>
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%0 Conference Proceedings
%T DialDoc 2022 Shared Task: Open-Book Document-grounded Dialogue Modeling
%A Feng, Song
%A Patel, Siva
%A Wan, Hui
%Y Feng, Song
%Y Wan, Hui
%Y Yuan, Caixia
%Y Yu, Han
%S Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F feng-etal-2022-dialdoc
%X The paper presents the results of the Shared Task hosted by the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering co-located at ACL 2022. The primary goal of this Shared Task is to build goal-oriented information-seeking conversation systems that are grounded in the domain documents, where each dialogue could correspond to multiple subtasks that are based on different documents. The task is to generate agent responses in natural language given the dialogue and document contexts. There are two task settings and leaderboards based on (1) the same sets of domains (SEEN) and (2) one unseen domain (UNSEEN). There are over 20 teams participating in Dev Phase and 8 teams participating in both Dev and Test Phases. Multiple submissions significantly outperform the baseline. The best-performing system achieves 52.06 F1 and the total of 191.30 on the SEEN task; and 34.65 F1 and the total of 130.79 on the UNSEEN task.
%R 10.18653/v1/2022.dialdoc-1.18
%U https://aclanthology.org/2022.dialdoc-1.18
%U https://doi.org/10.18653/v1/2022.dialdoc-1.18
%P 155-160

Markdown (Informal)

[DialDoc 2022 Shared Task: Open-Book Document-grounded Dialogue Modeling](https://aclanthology.org/2022.dialdoc-1.18) (Feng et al., dialdoc 2022)

• DialDoc 2022 Shared Task: Open-Book Document-grounded Dialogue Modeling (Feng et al., dialdoc 2022)

ACL

• Song Feng, Siva Patel, and Hui Wan. 2022. DialDoc 2022 Shared Task: Open-Book Document-grounded Dialogue Modeling. In Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering, pages 155–160, Dublin, Ireland. Association for Computational Linguistics.