Having an intelligent dialogue agent that can engage in conversational question answering (ConvQA) is now no longer limited to Sci-Fi movies only and has, in fact, turned into a reality. These intelligent agents are required to understand and correctly interpret the sequential turns provided as the context of the given question. However, these sequential questions are sometimes left implicit and thus require the resolution of some natural language phenomena such as anaphora and ellipsis. The task of question rewriting has the potential to address the challenges of resolving dependencies amongst the contextual turns by transforming them into intent-explicit questions. Nonetheless, the solution of rewriting the implicit questions comes with some potential challenges such as resulting in verbose questions and taking conversational aspect out of the scenario by generating self-contained questions. In this paper, we propose a novel framework, CONVSR (CONVQA using Structured Representations) for capturing and generating intermediate representations as conversational cues to enhance the capability of the QA model to better interpret the incomplete questions. We also deliberate how the strengths of this task could be leveraged in a bid to design more engaging and eloquent conversational agents. We test our model on the QuAC and CANARD datasets and illustrate by experimental results that our proposed framework achieves a better F1 score than the standard question rewriting model.

翻译：拥有能够参与对话问答（ConvQA）的智能对话代理已不再局限于科幻电影，事实上已成为现实。这些智能代理需要理解并正确解读作为给定问题上下文的连续轮次。然而，这些连续问题有时会隐含表达，因此需要解析某些自然语言现象，如回指和省略。问题重写任务有潜力通过将上下文轮次转化为意图显式的问题，来解决其间依赖关系的挑战。但重写隐含问题的解决方案也带来了一些潜在挑战，例如可能导致问题冗长，并通过生成自包含问题而削弱对话特性。本文提出了一种新颖框架CONVSR（利用结构化表示的对话问答），用于捕获并生成中间表示作为对话线索，以增强问答模型更好地解读不完整问题的能力。我们还探讨了如何利用此任务的优势来设计更具吸引力和表达力的对话代理。我们在QuAC和CANARD数据集上测试模型，实验结果表明，我们提出的框架相比标准问题重写模型取得了更优的F1分数。