The ability to handle miscommunication is crucial to robust and faithful
conversational AI. People usually deal with miscommunication immediately as
they detect it, using highly systematic interactional mechanisms called repair.
One important type of repair is Third Position Repair (TPR) whereby a speaker
is initially misunderstood but then corrects the misunderstanding as it becomes
apparent after the addressee's erroneous response. Here, we collect and
publicly release Repair-QA, the first large dataset of TPRs in a conversational
question answering (QA) setting. The data is comprised of the TPR turns,
corresponding dialogue contexts, and candidate repairs of the original turn for
execution of TPRs. We demonstrate the usefulness of the data by training and
evaluating strong baseline models for executing TPRs. For stand-alone TPR
execution, we perform both automatic and human evaluations on a fine-tuned T5
model, as well as OpenAI's GPT-3 LLMs. Additionally, we extrinsically evaluate
the LLMs' TPR processing capabilities in the downstream conversational QA task.
The results indicate poor out-of-the-box performance on TPR's by the GPT-3
models, which then significantly improves when exposed to Repair-QA.

处理不正常交流对于鲁棒和忠实的会话型人工智能至关重要。我们收集和公开发布了 Repair-QA，这是第一个大型的用于会话型问答设置中的第三位置修复的数据集。通过训练和评估强大的基准模型来展示数据的有效性，并且结果表明，GPT-3 模型在修复方面的性能从开箱即用时较差，但在接触到 Repair-QA 后显著改善。

应对会话式问答中的第三方修复

No that's not what I meant: Handling Third Position Repair in  Conversational Question Answering

Miscommunication and communication challenges between instructors and
students represents one of the primary barriers to post-secondary learning.
Students often avoid or miss opportunities to ask questions during office hours
due to insecurities or scheduling conflicts. Moreover, students need to work at
their own pace to have the freedom and time for the self-contemplation needed
to build conceptual understanding and develop creative thinking skills. To
eliminate barriers to student engagement, academic institutions need to
redefine their fundamental approach to education by proposing flexible
educational pathways that recognize continuous learning. To this end, we
developed an AI-augmented intelligent educational assistance framework based on
a power language model (i.e., GPT-3) that automatically generates
course-specific intelligent assistants regardless of discipline or academic
level. The virtual intelligent teaching assistant (TA) system will serve as a
voice-enabled helper capable of answering course-specific questions concerning
curriculum, logistics and course policies. It is envisioned to improve access
to course-related information for the students and reduce logistical workload
for the instructors and TAs. Its GPT-3-based knowledge discovery component as
well as the generalized system architecture is presented accompanied by a
methodical evaluation of the system accuracy and performance.

通过提供弹性的教育模式建议，基于 GPT-3 的智能教育辅助框架被研发，能够自动生成课程助手，可以回答与教学相关的问题，降低教师和助教的工作量同时提高学生获得课程相关信息的机会。