A neural network approach for knowledge-driven response generation
Pavlos Vougiouklis, Jonathon S. Hare, Elena Paslaru Bontas Simperl · ePrints Soton (University of Southampton) · 2016
We present a novel response generation system.The system assumes the hypothesis that participants in a conversation base their response not only on previous dialog utterances but also on their background knowledge.Our model is based on a Recurrent Neural Network (RNN) that is trained over concatenated sequences of comments, a Convolution Neural Network that is trained over Wikipedia sentences and a formulation that couples the two trained embeddings in a multimodal space.We create a dataset of aligned Wikipedia sentences and sequences of Reddit utterances, which we we use to train our model.Given a sequence of past utterances and a set of sentences that represent the background knowledge, our end-to-end learnable model is able to generate context-sensitive and knowledge-driven responses by leveraging the alignment of two different data sources.Our approach achieves up to 55% improvement in perplexity compared to purely sequential models based on RNNs that are trained only on sequences of utterances.