# Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Predictions

arXiv: https://arxiv.org/abs/2211.03524 · HTML: https://mlsanity.com/paper/2211.03524/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 37 references that are in the map.
- First posted on arXiv 7 Nov 2022.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [Multimodal Transformer for Unaligned Multimodal Language Sequences](https://mlsanity.com/paper/1906.00295/) (2019)
- [Bilateral Multi-Perspective Matching for Natural Language Sentences](https://mlsanity.com/paper/1702.03814/) (2017)
- [CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2109.09209/) (2021)
- [CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video Representations](https://mlsanity.com/paper/2109.14910/) (2021)
- [Self-Knowledge Distillation in Natural Language Processing](https://mlsanity.com/paper/1908.01851/) (2019)
- [Understanding Random Forests: From Theory to Practice](https://mlsanity.com/paper/1407.7502/) (2014)
- [Neural Topic Model via Optimal Transport](https://mlsanity.com/paper/2008.13537/) (2020)
- [Contrastive Learning for Neural Topic Model](https://mlsanity.com/paper/2110.12764/) (2021)
- [Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering](https://mlsanity.com/paper/2109.03381/) (2021)
- [Multimodal Categorization of Crisis Events in Social Media](https://mlsanity.com/paper/2004.04917/) (2020)
- [Topic-Aware Neural Keyphrase Generation for Social Media Language](https://mlsanity.com/paper/1906.03889/) (2019)
- [Pairwise Supervised Contrastive Learning of Sentence Representations](https://mlsanity.com/paper/2109.05424/) (2021)
- [CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks](https://mlsanity.com/paper/2112.02714/) (2021)
- [Improving Neural Cross-Lingual Summarization via Employing Optimal Transport Distance for Knowledge Distillation](https://mlsanity.com/paper/2112.03473/) (2021)
- [Enriching and Controlling Global Semantics for Text Summarization](https://mlsanity.com/paper/2109.10616/) (2021)
- [DialogueCSE: Dialogue-based Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2109.12599/) (2021)
- [SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning](https://mlsanity.com/paper/2209.05040/) (2022)
- [Using Argument-based Features to Predict and Analyse Review Helpfulness](https://mlsanity.com/paper/1707.07279/) (2017)
- [Predicting Helpfulness of Online Reviews](https://mlsanity.com/paper/2008.10129/) (2020)

## Built on by (most cited first, 13 of 13)

- [LLM Agents for Education: Advances and Applications](https://mlsanity.com/paper/2503.11733/) (2025)
- [InfoCTM: A Mutual Information Maximization Perspective of Cross-Lingual Topic Modeling](https://mlsanity.com/paper/2304.03544/) (2023)
- [KDMCSE: Knowledge Distillation Multimodal Sentence Embeddings with Adaptive Angular margin Contrastive Learning](https://mlsanity.com/paper/2403.17486/) (2024)
- [Topic Modeling as Multi-Objective Contrastive Optimization](https://mlsanity.com/paper/2402.07577/) (2024)
- [Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction](https://mlsanity.com/paper/2211.08238/) (2022)
- [Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning](https://mlsanity.com/paper/2305.13628/) (2023)
- [Encoding and Controlling Global Semantics for Long-form Video Question Answering](https://mlsanity.com/paper/2405.19723/) (2024)
- [MAMA: Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation Learning](https://mlsanity.com/paper/2407.03788/) (2024)
- [TI-JEPA: An Innovative Energy-based Joint Embedding Strategy for Text-Image Multimodal Systems](https://mlsanity.com/paper/2503.06380/) (2025)
- [Gradient-Boosted Decision Tree for Listwise Context Model in Multimodal Review Helpfulness Prediction](https://mlsanity.com/paper/2305.12678/) (2023)
- [Enhancing Multimodal Entity Linking with Jaccard Distance-based Conditional Contrastive Learning and Contextual Visual Augmentation](https://mlsanity.com/paper/2501.14166/) (2025)
- [PREMISE: Matching-based Prediction for Accurate Review Recommendation](https://mlsanity.com/paper/2505.01255/) (2025)
- [ViMRHP: A Vietnamese Benchmark Dataset for Multimodal Review Helpfulness Prediction via Human-AI Collaborative Annotation](https://mlsanity.com/paper/2505.07416/) (2025)

## Abstract (arXiv)

Modern Review Helpfulness Prediction systems are dependent upon multiple modalities, typically texts and images. Unfortunately, those contemporary approaches pay scarce attention to polish representations of cross-modal relations and tend to suffer from inferior optimization. This might cause harm to model's predictions in numerous cases. To overcome the aforementioned issues, we propose Multimodal Contrastive Learning for Multimodal Review Helpfulness Prediction (MRHP) problem, concentrating on mutual information between input modalities to explicitly elaborate cross-modal relations. In addition, we introduce Adaptive Weighting scheme for our contrastive learning approach in order to increase flexibility in optimization. Lastly, we propose Multimodal Interaction module to address the unalignment nature of multimodal data, thereby assisting the model in producing more reasonable multimodal representations. Experimental results show that our method outperforms prior baselines and achieves state-of-the-art results on two publicly available benchmark datasets for MRHP problem.