# ViMRHP: A Vietnamese Benchmark Dataset for Multimodal Review Helpfulness Prediction via Human-AI Collaborative Annotation

arXiv: https://arxiv.org/abs/2505.07416 · HTML: https://mlsanity.com/paper/2505.07416/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 12 May 2025.

## Builds on (most cited first, 16 of 16)

- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
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- [Is GPT-3 a Good Data Annotator?](https://mlsanity.com/paper/2212.10450/) (2022)
- [Large Language Models for Data Annotation and Synthesis: A Survey](https://mlsanity.com/paper/2402.13446/) (2024)
- [Want To Reduce Labeling Cost? GPT-3 Can Help](https://mlsanity.com/paper/2108.13487/) (2021)
- [AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators](https://mlsanity.com/paper/2303.16854/) (2023)
- [Bilateral Multi-Perspective Matching for Natural Language Sentences](https://mlsanity.com/paper/1702.03814/) (2017)
- [CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation](https://mlsanity.com/paper/2310.15638/) (2023)
- [Learning to Match Using Local and Distributed Representations of Text for Web Search](https://mlsanity.com/paper/1610.08136/) (2016)
- [Text Matching as Image Recognition](https://mlsanity.com/paper/1602.06359/) (2016)
- [Value FULCRA: Mapping Large Language Models to the Multidimensional Spectrum of Basic Human Values](https://mlsanity.com/paper/2311.10766/) (2023)
- [MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching](https://mlsanity.com/paper/1905.10289/) (2019)
- [Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Predictions](https://mlsanity.com/paper/2211.03524/) (2022)
- [SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning](https://mlsanity.com/paper/2209.05040/) (2022)
- [VIVA: A Benchmark for Vision-Grounded Decision-Making with Human Values](https://mlsanity.com/paper/2407.03000/) (2024)
- [On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction](https://mlsanity.com/paper/2303.00923/) (2023)

## Abstract (arXiv)

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