Conversational Question Generation (CQG) is a critical task for machines to
assist humans in fulfilling their information needs through conversations. The
task is generally cast into two different settings: answer-aware and
answer-unaware. While the former facilitates the models by exposing the
expected answer, the latter is more realistic and receiving growing attentions
recently. What-to-ask and how-to-ask are the two main challenges in the
answer-unaware setting. To address the first challenge, existing methods mainly
select sequential sentences in context as the rationales. We argue that the
conversation generated using such naive heuristics may not be natural enough as
in reality, the interlocutors often talk about the relevant contents that are
not necessarily sequential in context. Additionally, previous methods decide
the type of question to be generated (boolean/span-based) implicitly. Modeling
the question type explicitly is crucial as the answer, which hints the models
to generate a boolean or span-based question, is unavailable. To this end, we
present SG-CQG, a two-stage CQG framework. For the what-to-ask stage, a
sentence is selected as the rationale from a semantic graph that we construct,
and extract the answer span from it. For the how-to-ask stage, a classifier
determines the target answer type of the question via two explicit control
signals before generating and filtering. In addition, we propose Conv-Distinct,
a novel evaluation metric for CQG, to evaluate the diversity of the generated
conversation from a context. Compared with the existing answer-unaware CQG
models, the proposed SG-CQG achieves state-of-the-art performance.

SG-CQG 是一个用于回答无答案设置下如何生成与要求自然的对话的方法，它通过语义图和具有明确控制信号的分类器来解决 what-to-ask 和 how-to-ask 的挑战。同时，提出了新的评估指标 Conv-Distinct，并在实验中取得了最先进的表现。