Year: 2016
Type: conference-paper
Abstract: 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.
Institution: University of Southampton
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Nvidia
OpenAlex ID: W2566559207