@article{MDF349FB0,
title = "Knowledge Embedding Method for Implementing a Generative Question-Answering Chat System",
journal = "Journal of KIISE, JOK",
year = "2018",
issn = "2383-630X",
doi = "10.5626/JOK.2018.45.2.134",
author = "Sihyung Kim,Hyeon-gu Lee,Harksoo Kim",
keywords = "knowledge entity embedding,Siamese recurrent neural network,generative chat system,sequence-to-sequence model",
abstract = "A chat system is a computer program that understands user"s miscellaneous utterances and generates appropriate responses. Sometimes a chat system needs to answer users’ simple information-seeking questions. However, previous generative chat systems do not consider how to embed knowledge entities (i.e., subjects and objects in triple knowledge), essential elements for question-answering. The previous chat models have a disadvantage that they generate same responses although knowledge entities in users’ utterances are changed. To alleviate this problem, we propose a knowledge entity embedding method for improving question-answering accuracies of a generative chat system. The proposed method uses a Siamese recurrent neural network for embedding knowledge entities and their synonyms. For experiments, we implemented a sequence-to-sequence model in which subjects and predicates are encoded and objects are decoded. The proposed embedding method showed 12.48% higher accuracies than the conventional embedding method based on a convolutional neural network."
}