KSU Team’s Dialogue System at the NTCIR-13 Short Text Conversation Task 2

In this paper, the methods and results by the team KSU for STC-2 task at NTCIR-13 are described. We implemented both retrieval-based methods and a generation-based method. In the retrieval-based methods, a comment text with high similarity with the given utterance text is obtained from Yahoo! News comments data, and the reply text to the comment text is returned as the response to the input. Two methods were implemented with different information used for retrieval. It was confirmed that the precision of response selection was improved by selectively using some information on news articles on which the dialogue was based. In the generation-based method, we propose the Associative Conversation Model that generates visual information from textual information and uses it for generating sentences in order to utilize visual information in a dialogue system without image input. In research on Neural Machine Translation, there are studies that generate translated sentences using both images and sentences, and these studies show that visual information improves translation performance. However, it is not possible to use sentence generation algorithms using images for the dialogue systems since many text-based dialogue systems only accept text input. Our approach generates (associates) visual information from input text and generates response text using context vector fusing associative visual information and textual information. As a preliminary result, it was confirmed that visual information seemed to work effectively in several examples.