Efficient Language Model Construction for Spoken Dialog Systems by Inducting Language Resources of Different Languages

Since the quality of the language model directly affects the performance of the spoken dialog system (SDS), we should use a statistical language model (LM) trained with a large amount of data that is matched to the task domain. When porting an SDS to another language, however, it is costly to re-collect a large amount of user utterances in the target language. We thus use the language resources in a source language by utilizing statistical machine translation. The main challenge in this work is to induct automatic speech recognition results collected using a speechinput system that differs from the target SDS both in the task and the target language. To select appropriate sentences to be included in the training data for the LM, we induct a spoken language understanding module of the dialog system in the source language. Experimental construction using over three million user utterances showed that it is vital for selecting from the translation results.