Role Play Dialogue Aware Language Models Based on Conditional Hierarchical Recurrent Encoder-Decoder

We propose role play dialogue-aware language models (RPDALMs) that can leverage interactive contexts in role play multiturn dialogues for estimating the generative probability of words. Our motivation is to improve automatic speech recognition (ASR) performance in role play dialogues such as contact center dialogues and service center dialogues. Although long short-term memory recurrent neural network based language models (LSTM-RNN-LMs) can capture long-range contexts within an utterance, they cannot utilize sequential interactive information between speakers in multi-turn dialogues. Our idea is to explicitly leverage speakers’ roles of individual utterances, which are often available in role play dialogues, for neural language modeling. The RPDA-LMs are represented as a generative model conditioned by a role sequence of a target role play dialogue. We compose the RPDA-LMs by extending hierarchical recurrent encoder-decoder modeling so as to handle the role information. Our ASR evaluation in a contact center dialogue demonstrates that RPDA-LMs outperform LSTMRNN-LMs and document-context LMs in terms of perplexity and word error rate. In addition, we verify the effectiveness of explicitly taking interactive contexts into consideration.