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 cross-slot 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.
Bibliographic reference. Chen, Yun-Nung / Wang, William Yang / Rudnicky, Alexander I. (2015): "Learning semantic hierarchy with distributed representations for unsupervised spoken language understanding", In INTERSPEECH-2015, 1869-1873.