Argumentative dialogue is an important process where speakers discuss a specific theme for consensus building or decision making. In previous studies for generating consistent argumentative dialogue, retrieval-based methods with hand-crafted argumentation structures have been used. In this study, we propose a method to generate natural argumentative dialogues by combining an argumentation structure and language model. We trained the language model to rewrite a proposition of an argumentation structure on the basis of its information, such as keywords and stance, into the next utterance while considering its context, and we used the model to rewrite propositions in the argumentation structure. We manually evaluated the generated dialogues and found that the proposed method significantly improved the naturalness of dialogues without losing consistency of argumentation.

Social media texts, such as tweets from Twitter, contain many types of non-standard tokens, and the number of normalization approaches for handling such noisy text has been increasing. We present a method for automatically extracting pairs of a variant word and its normal form from unsegmented text on the basis of a pair-wise similarity approach. We incorporated the acquired variant-normalization pairs into Japanese morphological analysis. The experimental results show that our method can extract widely covered variants from large Twitter data and improve the recall of normalization without degrading the overall accuracy of Japanese morphological analysis.

This paper proposes a new method of constructing arbitrary class-based related word dictionaries on interactive topic models; we assume that each class is described by a topic. We propose a new semi-supervised method that uses the simplest topic model yielded by the standard EM algorithm; model calculation is very rapid. Furthermore our approach allows a dictionary to be modified interactively and the final dictionary has a hierarchical structure. This paper makes three contributions. First, it proposes a word-based semi-supervised topic model. Second, we apply the semi-supervised topic model to interactive learning; this approach is called the Interactive Topic Model. Third, we propose a score function; it extracts the related words that occupy the middle layer of the hierarchical structure. Experiments show that our method can appropriately retrieve the words belonging to an arbitrary class.