Quote Recommendation in Dialogue using Deep Neural Network

Information

Title Quote Recommendation in Dialogue using Deep Neural Network
Authors Hanbit Lee, Yeonchan Ahn, Haejun Lee, Seungdo Ha, Sang-goo Lee
Year 2016 / 7
Keywords quote recommendation, dialogue model, deep neural network
Acknowledgement Samsung Electronics
Publication Type International Conference
Publication The 39th International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR 2016), pp. 957-960
Link doi

Abstract

Quotes, or quotations, are well known phrases or sentences that we use for various purposes such as emphasis, elaboration, and humor. In this paper, we introduce a task of recommending quotes which are suitable for given dialogue context and we present a deep learning recommender system which combines recurrent neural network and convolutional neural network in order to learn semantic representation of each utterance and construct a sequence model for the dialog thread. We collected a large set of twitter dialogues with quote occurrences in order to evaluate proposed recommender system. Experimental results show that our approach outperforms not only the other state-of-the-art algorithms in quote recommendation task, but also other neural network based methods built for similar tasks.