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Found 5 papers, 1 papers with code
Short Text Topic Modeling with Topic Distribution Quantization and Negative Sampling Decoder
Topic models have been prevailing for many years on discovering latent semantics while modeling long documents.
Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learning
To overcome the data sparsity issue in short text topic modeling, existing methods commonly rely on data augmentation or the data characteristic of short texts to introduce more word co-occurrence information.
Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Predictions
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.
Vision-and-Language Pretraining
Inheriting the spirit of Transfer Learning, research works in V&L have devised multiple pretraining techniques on large-scale datasets in order to enhance the performance of downstream tasks.
