Hidden Softmax Sequence Model for Dialogue Structure Analysis
Zhiyang He, Xien Liu, Ping Lv, Ji Wu · 2016
We propose a new unsupervised learning model, hidden softmax sequence model (HSSM), based on Boltzmann machine for dialogue structure analysis.The model employs three types of units in the hidden layer to discovery dialogue latent structures: softmax units which represent latent states of utterances; binary units which represent latent topics specified by dialogues; and a binary unit that represents the global general topic shared across the whole dialogue corpus.In addition, the model contains extra connections between adjacent hidden softmax units to formulate the dependency between latent states.Two different kinds of real world dialogue corpora, Twitter-Post and AirTicketBooking, are utilized for extensive comparing experiments, and the results illustrate that the proposed model outperforms sate-ofthe-art popular approaches.