Abstract Machine understanding of questions is tightly related to recognition
of articulation in the context of the computational capabilities of an
underlying processing algorithm. In this paper a mathematical model to capture
and distinguish the latent structure in the articulation of questions is
presented. We propose an objective-driven approach to represent this latent
structure and show that such an approach is beneficial when examples of
complementary objectives are not available. We show that the latent structure
can be represented as a system that maximizes a cost function related to the
underlying objective. Further, we show that the optimization formulation can be
approximated to building a memory of patterns represented as a trained neural
auto-encoder. Experimental evaluation using many clusters of questions, each
related to an objective, shows 80% recognition accuracy and negligible false
positive across these clusters of questions. We then extend the same memory to
a related task where the goal is to iteratively refine a dataset of questions
based on the latent articulation. We also demonstrate a refinement scheme
called K-fingerprints, that achieves nearly 100% recognition with negligible
false positive across the different clusters of questions.

通过数学模型捕捉和区分问题表达中的潜在结构，提出目标驱动方法来代表和优化该结构，并通过训练神经自编码器来近似建立优化方案；实验结果表明，该方案在识别多个问题簇方面具有良好的准确性和极低的误报率，并通过 K-fingerprint 方法使得在不同问题簇中，近乎达到 100% 的识别率。