Stochastic finite state automata language model triggered by dialogue states
Yannick Estève, Frédéric Béchet, Alexis Nasr, Renato De Mori · 2001
Within the framework of Natural Spoken Dialogue systems, this paper describes a method for dynamically adapting a Language Model (LM) to the dialogue states detected. This LM combines a standard n-gram model with Stochastic Finite State Automata (SFSAs). During the training process, the sentence corpus used to train the LM is split into several hierarchical clusters in a 2-step process which involves both explicit knowledge and statistical criteria. All the clusters are stored in a binary tree where the whole corpus is attached to the root node. Each level of the tree corresponds to a higher specialization of the sub-corpora attached to the nodes and each node corresponds to a different dialogue state. From the same sentence corpus, SFSAs are extracted in order to model longer contexts than the ones used in the standard n-gram model. A set of SFSAs is attached to each node of the tree as well as a sub-LM which combines a bigram trained on the sub-corpus of the node and the SFSAs selected. A first decoding process calculates a word-graph as well as a first sentence hypothesis. This first hypothesis will be used to find the optimal node in the LM tree. Then, a rescoring process of the word graph using the LM attached to the node selected is performed. By adapting the LM to the dialogue state detected, we show a statistically significant gain in WER on a dialogue corpus collected by France Telecom R&D .