18 citations · 39 across the 11 of their papers we have counts for
12 papers
PREMISE: Matching-based Prediction for Accurate Review Recommendation
Wei Han, Hui Chen, Soujanya Poria
We present PREMISE (PREdict with Matching ScorEs), a new architecture for the matching-based learning in the multimodal fields for the multimodal review helpfulness (MRHP) task. Di…
MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research
Hui Chen, Miao Xiong, Yujie Lu +7
Recent advancements in AI agents have demonstrated their growing potential to drive and support scientific discovery. In this work, we introduce MLR-Bench, a comprehensive benchmar…
Two are better than one: Context window extension with multi-grained self-injection
Wei Han, Pan Zhou, Soujanya Poria +1
The limited context window of contemporary large language models (LLMs) remains a huge barrier to their broader application across various domains. While continual pre-training on…
Towards Robust Instruction Tuning on Multimodal Large Language Models
Wei Han, Hui Chen, Soujanya Poria
Fine-tuning large language models (LLMs) on multi-task instruction-following data has been proven to be a powerful learning paradigm for improving their zero-shot capabilities on n…
Self-Adaptive Sampling for Efficient Video Question-Answering on Image--Text Models
Wei Han, Hui Chen, Min-Yen Kan +1
Video question-answering is a fundamental task in the field of video understanding. Although current vision--language models (VLMs) equipped with Video Transformers have enabled te…
Domain-Expanded ASTE: Rethinking Generalization in Aspect Sentiment Triplet Extraction
Yew Ken Chia, Hui Chen, Wei Han +4
Aspect Sentiment Triplet Extraction (ASTE) is a challenging task in sentiment analysis, aiming to provide fine-grained insights into human sentiments. However, existing benchmarks…
MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and Accurate Inference on Missing Modality Sequences
Wei Han, Hui Chen, Min-Yen Kan +1
Existing multimodal tasks mostly target at the complete input modality setting, i.e., each modality is either complete or completely missing in both training and test sets. However…
SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training
Hui Chen, Wei Han, Soujanya Poria
Self-training methods have been explored in recent years and have exhibited great performance in improving semi-supervised learning. This work presents a Simple instance-Adaptive s…
SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning
Wei Han, Hui Chen, Zhen Hai +2
With the boom of e-commerce, Multimodal Review Helpfulness Prediction (MRHP), which aims to sort product reviews according to the predicted helpfulness scores has become a research…
DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification
Hui Chen, Wei Han, Diyi Yang +1
This paper proposes a simple yet effective interpolation-based data augmentation approach termed DoubleMix, to improve the robustness of models in text classification. DoubleMix fi…
Multi-trends Enhanced Dynamic Micro-video Recommendation
Yujie Lu, Yingxuan Huang, Shengyu Zhang +4
The explosively generated micro-videos on content sharing platforms call for recommender systems to permit personalized micro-video discovery with ease. Recent advances in micro-vi…
Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis
Wei Han, Hui Chen, Alexander Gelbukh +3
Multimodal sentiment analysis aims to extract and integrate semantic information collected from multiple modalities to recognize the expressed emotions and sentiment in multimodal…