构建越南语多模态评论有用性预测数据集，提升低资源语言推荐体验。
ViMRHP: A Vietnamese Benchmark Dataset for Multimodal Review Helpfulness Prediction via Human-AI Collaborative Annotation
- 通过人机协作标注，高效构建越南语多模态评论数据集。
- 46,000条评论覆盖2,000个商品，标注效率提升约75%。
- 适合关注低资源语言、多模态推荐与人机协同标注的研究者。
多模态评论有用性预测（MRHP）是推荐系统中的关键任务，尤其在电商平台中至关重要。判断用户生成评论的有用性可显著提升用户体验并辅助消费决策。然而，现有数据集主要集中在英语和印尼语，缺乏语言多样性，尤其对越南语等低资源语言支持不足。本文提出ViMRHP（越南语多模态评论有用性预测）数据集，一个大规模基准数据集，涵盖四个领域，包含2,000个商品及46,000条评论。为降低标注成本与时间，采用AI辅助标注，使每项任务耗时从90至120秒降至20至40秒，整体成本下降约65%，同时保持数据质量。通过对比人类验证与AI生成标注结果，评估基线模型性能差异，揭示了复杂任务中AI标注的局限性。该数据集已公开于https://github.com/trng28/ViMRHP。
原文摘要 · Abstract (English)
Multimodal Review Helpfulness Prediction (MRHP) is an essential task in recommender systems, particularly in E-commerce platforms. Determining the helpfulness of user-generated reviews enhances user experience and improves consumer decision-making. However, existing datasets focus predominantly on English and Indonesian, resulting in a lack of linguistic diversity, especially for low-resource languages such as Vietnamese. In this paper, we introduce ViMRHP (Vietnamese Multimodal Review Helpfulness Prediction), a large-scale benchmark dataset for MRHP task in Vietnamese. This dataset covers four domains, including 2K products with 46K reviews. Meanwhile, a large-scale dataset requires considerable time and cost. To optimize the annotation process, we leverage AI to assist annotators in constructing the ViMRHP dataset. With AI assistance, annotation time is reduced (90 to 120 seconds per task down to 20 to 40 seconds per task) while maintaining data quality and lowering overall costs by approximately 65%. However, AI-generated annotations still have limitations in complex annotation tasks, which we further examine through a detailed performance analysis. In our experiment on ViMRHP, we evaluate baseline models on human-verified and AI-generated annotations to assess their quality differences. The ViMRHP dataset is publicly available at https://github.com/trng28/ViMRHP
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