In this work, we propose contextual language models that incorporate dialog level discourse information into language modeling. Previous works on contextual language model treat preceding utterances as a sequence of inputs, without considering dialog interactions. We design recurrent neural network (RNN) based contextual language models that specially track the interactions between speakers in a dialog. Experiment results on Switchboard Dialog Act Corpus show that the proposed model outperforms conventional single turn based RNN language model by 3.3% on perplexity. The proposed models also demonstrate advantageous performance over other competitive contextual language models.
In study REF , they proposed contextual RNN language models that track the interactions between speakers by using additional RNN.
Dialog context language modeling with recurrent neural networks
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Computer Science