DPAL-BERT:A Faster and Lighter Question Answering Model

Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced efficiency.This study aims to minimize the inference time of such models while maintaining computational performance.It also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and DPAL-BERTC.This research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge transfer.The experimental results showed that the distilled models greatly outperform models trained from scratch.In addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the original.This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.

Triple Multimodal Cyclic Fusion and Self-Adaptive Balancing for Video Q&A Systems

Performance of Video Question and Answer(VQA)systems relies on capturing key information of both visual images and natural language in the context to generate relevant questions’answers.However,traditional linear combinations of multimodal features focus only on shallow feature interactions,fall far short of the need of deep feature fusion.Attention mechanisms were used to perform deep fusion,but most of them can only process weight assignment of single-modal information,leading to attention imbalance for different modalities.To address above problems,we propose a novel VQA model based on Triple Multimodal feature Cyclic Fusion(TMCF)and Self-AdaptiveMultimodal Balancing Mechanism(SAMB).Our model is designed to enhance complex feature interactions among multimodal features with cross-modal information balancing.In addition,TMCF and SAMB can be used as an extensible plug-in for exploring new feature combinations in the visual image domain.Extensive experiments were conducted on MSVDQA and MSRVTT-QA datasets.The results confirm the advantages of our approach in handling multimodal tasks.Besides,we also provide analyses for ablation studies to verify the effectiveness of each proposed component.