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 on-line event
and featured a shared task on conversational question answering. Since all of
the participant teams experimented with answer generation models for this task,
we identified evaluation of answer correctness in this settings as the major
challenge and a current 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.

本文介绍了 Search-Oriented Conversational AI（SCAI）会议的举办与会议中的共享任务，即探讨对话问答中回答正确性的自动评估和人工众包实验，并且参与团队的系统为评估数据集扩充了替代正确答案。

SCAI-QReCC 对话问答共享任务

SCAI-QReCC Shared Task on Conversational Question Answering

In conversational question answering, systems must correctly interpret the
interconnected interactions and generate knowledgeable answers, which may
require the retrieval of relevant information from a background repository.
Recent approaches to this problem leverage neural language models, although
different alternatives can be considered in terms of modules for (a)
representing user questions in context, (b) retrieving the relevant background
information, and (c) generating the answer. This work presents a conversational
question answering system designed specifically for the Search-Oriented
Conversational AI (SCAI) shared task, and reports on a detailed analysis of its
question rewriting module. In particular, we considered different variations of
the question rewriting module to evaluate the influence on the subsequent
components, and performed a careful analysis of the results obtained with the
best system configuration. Our system achieved the best performance in the
shared task and our analysis emphasizes the importance of the conversation
context representation for the overall system performance.

本文研究了基于神经语言模型、以及不同的信息提取和答案生成方式，在特定的搜索导向的对话人工智能任务中，如何通过代表问题上下文的不同重写模块，从背景知识库中提取信息，生成知识回答，最终取得了最佳表现，并在分析中强调了对话上下文的重要性。