The current problem in building a conversational model from Twitter data is the scarcity of long conversations. According to our statistics, more than 90% of conversations in Twitter are composed of just two tweets. Previous work has utilized only conversations lasting longer than three tweets for dialogue modeling so that more than a single interaction can be successfully modeled. This paper verifies, by experiment, that two-tweet exchanges alone can lead to conversational models that are comparable to those made from longer-tweet conversations. This finding leverages the value of Twitter as a dialogue corpus and opens the possibility of better conversational modeling using Twitter data.

Unsupervised clustering of utterances can be useful for the modeling of dialogue acts for dialogue applications. Previously, the Chinese restaurant process (CRP), a non-parametric Bayesian method, has been introduced and has shown promising results for the clustering of utterances in dialogue. This paper newly introduces the infinite HMM, which is also a nonparametric Bayesian method, and verifies its effectiveness. Experimental results in two dialogue domains show that the infinite HMM, which takes into account the sequence of utterances in its clustering process, significantly outperforms the CRP. Although the infinite HMM outperformed other methods, we also found that clustering complex dialogue data, such as humanhuman conversations, is still hard when compared to humanmachine dialogues. Index Terms: Unsupervised clustering, Nonparametric Bayesian methods, Chinese restaurant process, Infinite HMM

Television sets and video game consoles equipped with a web browser have appeared, and we are now able to browse web pages on television screens. However, existing navigation techniques are too difficult in this situation. In this paper, we propose Arrow Tag, a new link selection technique for web browsers on TV. In this technique, sequences of arrow signs called Arrow Tags are assigned to the links of the web pages, so users can select the links by pushing the four direction keys a few times, while keeping her/his gaze fixed on the television screen. User studies show that Arrow Tag significantly outperforms the conventional techniques of Focus Move and Number Tag. Moreover, most participants preferred Arrow Tag over either Focus Move or Number Tag.