We propose a new deep neural network architecture, TabNet, for table type classification. Table type is essential information for exploring the power of Web tables, and it is important to understand the semantic structures of tables in order to classify them correctly. A table is a matrix of texts, analogous to an image, which is a matrix of pixels, and each text consists of a sequence of tokens. Our hybrid architecture mirrors the structure of tables: its recurrent neural network (RNN) encodes a sequence of tokens for each cell to create a 3d table volume like image data, and its convolutional neural network (CNN) captures semantic features, e.g., the existence of rows describing properties, to classify tables. Experiments using Web tables with various structures and topics demonstrated that TabNet achieved considerable improvements over state-of-the-art methods specialized for table classification and other deep neural network architectures.

We have been studying methods to personalize system utterances for users in casual conversations.We know that personalization is important, but no wellestablished way to personalize system utterances for users has been proposed.In this paper, we report the results of our experiment that examined how humans personalize utterances when speaking to each other in casual conversations.In particular, we elicited post-dialogue comments from speakers and analyzed the comments to determine what they thought about the dialogues while they engaged in them.In addition, by analyzing the effectiveness of their thoughts, we found that dialogue strategies for personalization related to "topic elaboration", "topic changing" and "tempo" significantly increased the satisfaction with regard to the dialogues.

One of the key ways of making dialogue agents more attractive as conversation partners is characterization, as it makes the agents more friendly, humanlike, and entertaining.To build such characters, utterances suitable for the characters are usually manually prepared.However, it is expensive to do this for a large number of utterances.To reduce this cost, we are developing a natural language generator that can express the linguistic styles of particular characters.To this end, we analyze the linguistic peculiarities of Japanese fictional characters (such as those in cartoons or comics and mascots), which have strong characteristics.The contributions of this study are that we (i) present comprehensive categories of linguistic peculiarities of Japanese fictional characters that cover around 90% of such characters' linguistic peculiarities and (ii) reveal the impact of each category on characterizing dialogue system utterances.

This paper describes an automatic content indexing system for news programs, with a special emphasis on its segmentation process. The process can successfully segment an entire news program into topic-centered news stories; the primary tool is a linguistic topic segmentation algorithm. Experiments show that the resulting speech-based segments are fairly accurate, and scene change points supplied by an external video processor can be of help in improving segmentation effectiveness.