The increasing demand for the web-based digital assistants has given a rapid rise in the interest of the Information Retrieval (IR) community towards the field of conversational question answering (ConvQA). However, one of the critical aspects of ConvQA is the effective selection of conversational history turns to answer the question at hand. The dependency between relevant history selection and correct answer prediction is an intriguing but under-explored area. The selected relevant context can better guide the system so as to where exactly in the passage to look for an answer. Irrelevant context, on the other hand, brings noise to the system, thereby resulting in a decline in the model's performance. In this paper, we propose a framework, DHS-ConvQA (Dynamic History Selection in Conversational Question Answering), that first generates the context and question entities for all the history turns, which are then pruned on the basis of similarity they share in common with the question at hand. We also propose an attention-based mechanism to re-rank the pruned terms based on their calculated weights of how useful they are in answering the question. In the end, we further aid the model by highlighting the terms in the re-ranked conversational history using a binary classification task and keeping the useful terms (predicted as 1) and ignoring the irrelevant terms (predicted as 0). We demonstrate the efficacy of our proposed framework with extensive experimental results on CANARD and QuAC -- the two popularly utilized datasets in ConvQA. We demonstrate that selecting relevant turns works better than rewriting the original question. We also investigate how adding the irrelevant history turns negatively impacts the model's performance and discuss the research challenges that demand more attention from the IR community.

翻译：基于网络的数字助手需求的日益增长，引起了信息检索（IR）社区对对话式问答（ConvQA）领域的浓厚兴趣。然而，ConvQA的关键方面之一是有效选择对话历史轮次以回答当前问题。相关历史选择与正确答案预测之间的依赖关系是一个有趣但尚未充分探索的领域。选定的相关上下文可以更好地引导系统，使其精确地定位段落中需要查找答案的位置。另一方面，不相关的上下文会给系统带来噪声，从而导致模型性能下降。在本文中，我们提出一个框架DHS-ConvQA（对话式问答中的动态历史选择），该框架首先生成所有历史轮次的上下文和问题实体，然后根据它们与当前问题的相似度进行修剪。我们还提出了一种基于注意力的机制，根据计算出的权重（即它们在回答问题中的有用程度）对修剪后的术语进行重新排序。最后，我们通过二元分类任务突出显示重新排序的对话历史中的术语，保留有用术语（预测为1）并忽略无关术语（预测为0），从而进一步辅助模型。我们在ConvQA中常用的两个数据集CANARD和QuAC上进行了广泛的实验，证明了所提出框架的有效性。我们证明，选择相关轮次比重写原始问题效果更好。我们还研究了添加不相关历史轮次如何负面影响模型性能，并讨论了需要IR社区更多关注的研究挑战。