Learning semantic hierarchy with distributed representations for unsupervised spoken language understanding

We study the problem of unsupervised ontology learning for semantic understanding in spoken dialogue systems, in particular, learning the hierarchical semantic structure from the data. Given unlabelled conversations, we augment a frame-semantic based unsupervised slot induction approach with hierarchical agglomerative clustering to merge topically-related slots (e.g., both slots “direction” and “locale” convey location-related information) for building a coherent semantic hierarchy, and then estimate the slot importance at different levels. The high-level semantic estimation involves not only within-slot but also crossslot relations. The experiments show that high-level semantic information can accurately estimate the prominence of slots, significantly improving the slot induction performance; furthermore, a semantic decoder trained on the data with automatically extracted slots achieves about 68% F-measure, which is close to the one from hand-crafted grammars.