情感与文化智能AI研讨会情感预测竞赛解决方案
Solution for Emotion Prediction Competition of Workshop on Emotionally and Culturally Intelligent AI
AI总结：
本文针对WECIA情感预测竞赛中ArtELingo数据集的模态不平衡和语言文化差异问题，提出结合单模态增强与情感-文化特定提示的ECSP方法，最终以0.627分获得第一名。
摘要：
本报告详细描述了我们探索并提出于WECIA情感预测竞赛（EPC）中的方法，该竞赛旨在通过一件艺术作品及其评论来预测一个人的情感。该竞赛的数据集为ArtELingo，旨在鼓励跨语言与文化多样性的研究。该数据集存在两大主要挑战，即模态不平衡问题和语言文化差异问题。为解决这一问题，我们提出了一种简单而有效的方法，称为带情感-文化特定提示的单-多模态方法（ECSP），其重点是利用单模态信息来增强多模态模型的性能，并采用精心设计的提示来减少文化差异问题。具体而言，我们的方法包含两个主要模块：（1）基于XLM-R的单模态模型和基于X$^2$-VLM的多模态模型；（2）情感-文化特定提示。我们的方法在最终测试中以0.627的分数排名第一。
英文摘要：
This report provide a detailed description of the method that we explored and proposed in the WECIA Emotion Prediction Competition (EPC), which predicts a person's emotion through an artistic work with a comment. The dataset of this competition is ArtELingo, designed to encourage work on diversity across languages and cultures. The dataset has two main challenges, namely modal imbalance problem and language-cultural differences problem. In order to address this issue, we propose a simple yet effective approach called single-multi modal with Emotion-Cultural specific prompt(ECSP), which focuses on using the single modal message to enhance the performance of multimodal models and a well-designed prompt to reduce cultural differences problem. To clarify, our approach contains two main blocks: (1)XLM-R\cite{conneau2019unsupervised} based unimodal model and X$^2$-VLM\cite{zeng2022x} based multimodal model (2) Emotion-Cultural specific prompt. Our approach ranked first in the final test with a score of 0.627.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
