This paper explains the participation of team Hitachi to SemEval-2023 Task 3 “Detecting the genre, the framing, and the persuasion techniques in online news in a multi-lingual setup.” Based on the multilingual, multi-task nature of the task and the low-resource setting, we investigated different cross-lingual and multi-task strategies for training the pretrained language models. Through extensive experiments, we found that (a) cross-lingual/multi-task training, and (b) collecting an external balanced dataset, can benefit the genre and framing detection. We constructed ensemble models from the results and achieved the highest macro-averaged F1 scores in Italian and Russian genre categorization subtasks.

This paper describes our participation in SemEval-2023 Task 4, ValueEval: Identification of Human Values behind Arguments. The aim of this task is to identify whether or not an input text supports each of the 20 pre-defined human values. Previous work on human value detection has shown the effectiveness of a sequence classification approach using BERT. However, little is known about what type of task formulation is suitable for the task. To this end, this paper explores various task formulations, including sequence classification, question answering, and question answering with chain-of-thought prompting and evaluates their performances on the shared task dataset. Experiments show that a zero-shot approach is not as effective as other methods, and there is no one approach that is optimal in every scenario. Our analysis also reveals that utilizing the descriptions of human values can help to improve performance.

Utilizing citations for research artifacts (e.g., dataset, software) in scholarly papers contributes to efficient expansion of research artifact repositories and various applications e.g., the search, recommendation, and evaluation of such artifacts. This study focuses on citations using URLs (URL citations) and aims to identify and analyze research artifact citations automatically. This paper addresses the classification task for each URL citation to identify (1) the role that the referenced resources play in research activities, (2) the type of referenced resources, and (3) the reason why the author cited the resources. This paper proposes the classification method using section titles and footnote texts as new input features. We extracted URL citations from international conference papers as experimental data. We performed 5-fold cross-validation using the data and computed the classification performance of our method. The results demonstrate that our method is effective in all tasks. An additional experiment demonstrates that using cited URLs as input features is also effective.