论文标题
保持饥饿，保持专注：在寻求信息的对话中引起信息和具体问题
Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations
论文作者
论文摘要
我们研究了在信息 - 对称对话中引起信息性问题的问题。与以前关于问题生成的工作不同，在很大程度上假设答案可能是什么，我们对未给出答案的上下文的情况感兴趣，但必须务实地务实地推理如何获取新信息，鉴于共享的对话历史记录。我们确定了两个核心挑战：（1）正式定义潜在问题的信息性，（2）探索潜在问题的大量空间以找到好的候选人。为了产生务实的问题，我们使用加强学习来优化我们建议的信息度量，并结合了旨在促进更具体问题的奖励功能。我们证明，由此产生的务实的发问者大大提高了基线模型中产生的问题的信息和特异性，这是我们的指标和人类评估的。
We investigate the problem of generating informative questions in information-asymmetric conversations. Unlike previous work on question generation which largely assumes knowledge of what the answer might be, we are interested in the scenario where the questioner is not given the context from which answers are drawn, but must reason pragmatically about how to acquire new information, given the shared conversation history. We identify two core challenges: (1) formally defining the informativeness of potential questions, and (2) exploring the prohibitively large space of potential questions to find the good candidates. To generate pragmatic questions, we use reinforcement learning to optimize an informativeness metric we propose, combined with a reward function designed to promote more specific questions. We demonstrate that the resulting pragmatic questioner substantially improves the informativeness and specificity of questions generated over a baseline model, as evaluated by our metrics as well as humans.
