Fact-centric information needs are rarely one-shot; users typically ask follow-up questions to explore a topic. In such a conversational setting, the user's inputs are often incomplete, with entities or predicates left out, and ungrammatical phrases. This poses a huge challenge to question answering (QA) systems that typically rely on cues in full-fledged interrogative sentences. As a solution, we develop CONVEX: an unsupervised method that can answer incomplete questions over a knowledge graph (KG) by maintaining conversation context using entities and predicates seen so far and automatically inferring missing or ambiguous pieces for follow-up questions. The core of our method is a graph exploration algorithm that judiciously expands a frontier to find candidate answers for the current question. To evaluate CONVEX, we release ConvQuestions, a crowdsourced benchmark with 11,200 distinct conversations from five different domains. We show that CONVEX: (i) adds conversational support to any stand-alone QA system, and (ii) outperforms state-of-the-art baselines and question completion strategies.
翻译:以事实为中心的信息需求很少是一次性的;用户通常会问后续问题,以探讨一个专题。在这样的对话环境中,用户的投入往往不完整,实体或上游被遗漏,还有非语法短语。这给通常依赖全面审讯判决线索的回答(QA)系统提出了巨大的质疑。作为一种解决办法,我们开发了CONVEX:一种不受监督的方法,它可以通过使用迄今所见的实体和上游来保持对话背景,在知识图表(KG)上回答不完整的问题,并自动推断后续问题缺失或模糊。我们的方法的核心是图表探索算法,它明智地扩展了为当前问题寻找候选人答案的前沿。为了评估CONVEX,我们发布了Convission,这是一个由11 200个不同领域不同对话组成的群集基准。我们显示,CONVEX:(一) 对任何独立的QA系统增加谈话支持,以及(二) 超越了最新基线和问题完成战略。