Multimodal Intermediate-Task Training Effects on Zero-Shot Cross-Lingual Sentiment Analysis Performance

This paper describes our system developed for the SemEval-2023 Task 12 "Sentiment Analysis for Low-resource African Languages using Twitter Dataset". Sentiment analysis is one of the most widely studied applications in natural language processing. However, most prior work still focuses on a small number of high-resource languages. Building reliable sentiment analysis systems for low-resource languages remains challenging, due to the limited training data in this task. In this work, we propose to leverage language-adaptive and task-adaptive pretraining on African texts and study transfer learni

Research goal: What is the impact of multimodal intermediate-task training (e.g., image-text alignment) on zero-shot cross-lingual transfer performance for sentiment analysis, as measured by F1 score differences across low-resource languages in the XTREME-R benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.