论文标题
从重写到记忆：对话质量检查模型的共同基础
From Rewriting to Remembering: Common Ground for Conversational QA Models
论文作者
论文摘要
在会话质量检查中，模型必须利用上一轮的信息来回答即将到来的问题。当前的方法（例如重写问题）很难在对话放松时提取相关信息。我们介绍了共同点（CG），这是一种在出现时积累会话信息的方法，并在每个转弯时选择相关信息。我们表明，与现有方法相比，CG提供了一种更有效，更类似的方式来利用对话信息，从而改善了开放域对话质量质量质量质量质量质量质量量。
In conversational QA, models have to leverage information in previous turns to answer upcoming questions. Current approaches, such as Question Rewriting, struggle to extract relevant information as the conversation unwinds. We introduce the Common Ground (CG), an approach to accumulate conversational information as it emerges and select the relevant information at every turn. We show that CG offers a more efficient and human-like way to exploit conversational information compared to existing approaches, leading to improvements on Open Domain Conversational QA.
