Given a natural language query, teaching machines to ask clarifying questions
is of immense utility in practical natural language processing systems. Such
interactions could help in filling information gaps for better machine
comprehension of the query. For the task of ranking clarification questions, we
hypothesize that determining whether a clarification question pertains to a
missing entry in a given post (on QA forums such as StackExchange) could be
considered as a special case of Natural Language Inference (NLI), where both
the post and the most relevant clarification question point to a shared latent
piece of information or context. We validate this hypothesis by incorporating
representations from a Siamese BERT model fine-tuned on NLI and Multi-NLI
datasets into our models and demonstrate that our best performing model obtains
a relative performance improvement of 40 percent and 60 percent respectively
(on the key metric of Precision@1), over the state-of-the-art baseline(s) on
the two evaluation sets of the StackExchange dataset, thereby, significantly
surpassing the state-of-the-art.

教机器提出澄清问题对于自然语言处理系统非常有用，本研究使用 Siamese BERT 模型，将 NLI 的特征运用在 StackExchange 数据集问题排序上，取得了相对于现有基线模型高达 40% 至 60% 的性能提升。