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        <title>SCAI-QReCC Shared Task on Conversational Question Answering</title>
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        <namePart type="given">Svitlana</namePart>
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    <abstract>Search-Oriented Conversational AI (SCAI) is an established venue that regularly puts a spotlight upon the recent work advancing the field of conversational search. SCAI’21 was organised as an independent online event and featured a shared task on conversational question answering, on which this paper reports. The shared task featured three subtasks that correspond to three steps in conversational question answering: question rewriting, passage retrieval, and answer generation. This report discusses each subtask, but emphasizes the answer generation subtask as it attracted the most attention from the participants and we identified evaluation of answer correctness in the conversational settings as a major challenge and acurrent research gap. Alongside the automatic evaluation, we conducted two crowdsourcing experiments to collect annotations for answer plausibility and faithfulness. As a result of this shared task, the original conversational QA dataset used for evaluation was further extended with alternative correct answers produced by the participant systems.</abstract>
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%0 Conference Proceedings
%T SCAI-QReCC Shared Task on Conversational Question Answering
%A Vakulenko, Svitlana
%A Kiesel, Johannes
%A Fröbe, Maik
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F vakulenko-etal-2022-scai
%X Search-Oriented Conversational AI (SCAI) is an established venue that regularly puts a spotlight upon the recent work advancing the field of conversational search. SCAI’21 was organised as an independent online event and featured a shared task on conversational question answering, on which this paper reports. The shared task featured three subtasks that correspond to three steps in conversational question answering: question rewriting, passage retrieval, and answer generation. This report discusses each subtask, but emphasizes the answer generation subtask as it attracted the most attention from the participants and we identified evaluation of answer correctness in the conversational settings as a major challenge and acurrent research gap. Alongside the automatic evaluation, we conducted two crowdsourcing experiments to collect annotations for answer plausibility and faithfulness. As a result of this shared task, the original conversational QA dataset used for evaluation was further extended with alternative correct answers produced by the participant systems.
%U https://aclanthology.org/2022.lrec-1.525
%P 4913-4922

Markdown (Informal)

[SCAI-QReCC Shared Task on Conversational Question Answering](https://aclanthology.org/2022.lrec-1.525) (Vakulenko et al., LREC 2022)

• SCAI-QReCC Shared Task on Conversational Question Answering (Vakulenko et al., LREC 2022)

ACL

• Svitlana Vakulenko, Johannes Kiesel, and Maik Fröbe. 2022. SCAI-QReCC Shared Task on Conversational Question Answering. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 4913–4922, Marseille, France. European Language Resources Association.