Conversations are often held in laboratories and companies. A summary is vital to grasp the content of a discussion for people who did not attend the discussion. If the summary is illustrated as an argument structure, it is helpful to grasp the discu
ssion's essentials immediately. Our purpose in this paper is to predict a link structure between nodes that consist of utterances in a conversation: classification of each node pair into linked'' or not-linked.'' One approach to predict the structure is to utilize machine learning models. However, the result tends to over-generate links of nodes. To solve this problem, we introduce a two-step method to the structure prediction task. We utilize a machine learning-based approach as the first step: a link prediction task. Then, we apply a score-based approach as the second step: a link selection task. Our two-step methods dramatically improved the accuracy as compared with one-step methods based on SVM and BERT.
This study aimed at investigating the effect of dialogue and debate strategy in the
Development of speaking skills of the Jordanian fourth Grade students in North eastern
Badia Directorate of Education.
This study used the qusai - experimental des
ign along with the descriptive method in
term of using pre_ post tests and a questionnaire Male and female teachers. The sample of
the study consisted of (108) Jordanian fourth grade students and (68) male and female
Arabic language teachers who teach the fourth grade in north eastern Badia directorate of
education in the academic year 2017_2018.
to achieve the purpose of study the researcher distributed the sample of the study
into tow experimental groups and two control groups. to achieve the purpose of the study
descriptive statistics and MONCOVA were used. The results of the study showed that the
experimental groups out performance and it was in favor of females also.
