- Yasutomo Kimura, Hideyuki Shibuki, Hokuto Ototake, Yuzu Uchida, Keiichi Takamaru, Kotaro Sakamoto, Madoka Ishioroshi, T. Mitamura, N. Kando. 2019. Influence of Classifiers and Encoders on Argument Classification in Japanese Assembly Minutes. Abstract: We performed a comparative study of the influence of seven different types of classifiers and four types of encoders on argument classification in Japanese assembly minutes using 45 sets of results from the Question Answering Lab for Political Information task at the NTCIR-14 workshop. The more accurate value obtained from a classification of argumentative relations between a speech sentence and a political topic was 0.942 using the support vector machines classifier and one-hot encoding, while the most accurate classification value obtained with the long short-term memory classifier and word embedding was estimated to be 0.934.
