Conversational Question Answering (CQA) aims to answer questions contained
within dialogues, which are not easily interpretable without context.
Developing a model to rewrite conversational questions into self-contained ones
is an emerging solution in industry settings as it allows using existing
single-turn QA systems to avoid training a CQA model from scratch. Previous
work trains rewriting models using human rewrites as supervision. However, such
objectives are disconnected with QA models and therefore more human-like
rewrites do not guarantee better QA performance. In this paper we propose using
QA feedback to supervise the rewriting model with reinforcement learning.
Experiments show that our approach can effectively improve QA performance over
baselines for both extractive and retrieval QA. Furthermore, human evaluation
shows that our method can generate more accurate and detailed rewrites when
compared to human annotations.

使用强化学习训练重写模型，以 QA 反馈作为监督信号，能够有效提高抽取式和检索式问答中的性能，并且人工评估表明该方法能够生成更准确和详细的重写，优于人工注释。

会话式问答中的加强问句重述技术

Reinforced Question Rewriting for Conversational Question Answering

Machine-in-the-loop writing aims to enable humans to collaborate with models
to complete their writing tasks more effectively. Prior work has found that
providing humans a machine-written draft or sentence-level continuations has
limited success since the generated text tends to deviate from humans'
intention. To allow the user to retain control over the content, we train a
rewriting model that, when prompted, modifies specified spans of text within
the user's original draft to introduce descriptive and figurative elements
locally in the text. We evaluate the model on its ability to collaborate with
humans on the task of creative image captioning. On a user study through Amazon
Mechanical Turk, our model is rated to be more helpful than a baseline
infilling language model. In addition, third-party evaluation shows that users
write more descriptive and figurative captions when collaborating with our
model compared to completing the task alone.

该研究旨在通过机器辅助写作使人类更有效地完成写作任务；研究者提出一种重写模型，允许用户在原始草稿的指定文本范围内引入局部描述性和比喻性元素，并评估模型在创意图像字幕任务上协作的能力。在与传统模型和用户单独完成任务的比较中，模型获得更高评价，并促使用户编写更具有描述性和比喻性的字幕。