This paper presents the systems and approaches of the Gallagher team for the SemEval-2023 Task 5: Clickbait Spoiling. We propose a method to classify the type of spoiler (phrase, passage, multi) and a question-answering method to generate spoilers that satisfy the curiosity caused by clickbait posts. We experiment with the state-of-the-art Seq2Seq model T5. To identify the spoiler types we used a fine-tuned T5 classifier (Subtask 1). A mixture of T5 and Flan-T5 was used to generate the spoilers for clickbait posts (Subtask 2). Our system officially ranks first in generating phrase type spoilers in Subtask 2, and achieves the highest precision score for passage type spoilers in Subtask 1.

This paper presents the systems and approaches of the Arizonans team for the SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis. We finetune the Multilingual RoBERTa model trained with about 200M tweets, XLM-T. Our final model ranked 9th out of 45 overall, 13th in seen languages, and 8th in unseen languages.