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.

CONVEX 是一种无监督方法，可以通过维护对话上下文使用到目前为止看到的实体和谓词并自动推断缺失或模棱两可的问题部分，回答知识图上的不完整问题。