False information detection can detect false information in social media and reduce its negative impact on society. With the development of multimedia, the multimodal content contained in false information is increasing, so it is important to use multimodal features to detect false information. This paper mainly uses information from two modalities, text and image. The features extracted by the backbone network are not further processed in the previous work, and the problems of noise and information loss in the process of fusing multimodal features are ignored. This paper proposes a false information detection method based on Text-CNN and SE modules. We use Text-CNN to process the text and image features extracted by BERT and Swin-transformer to enhance the quality of the features. In addition, we use the modified SE module to fuse text and image features and reduce the noise in the fusion process. Meanwhile, we draw on the idea of residual networks to reduce information loss in the fusion process by concatenating the original features with the fused features. Our model improves accuracy by 6.5% and 2.0% on the Weibo dataset and Twitter dataset compared to the attention based multimodal factorized bilinear pooling. The comparative experimental results show that the proposed model can improve the accuracy of false information detection. The results of ablation experiments further demonstrate the effectiveness of each module in our model.

Transfer learning plays a crucial role in low-resource machine translation by addressing the challenge of poor model performance due to limited data in low-resource languages, thereby improving translation accuracy. Current research methods not only utilize pre-trained parent models for parameter initialization and fine-tuning but also use the soft labels output by these parent models to enhance the consistency between parent and child models. However, even if the parent model performs well, there are still instances where certain token predictions are unstable. During training, if the child model incorporates these unstable token predictions, it can hinder its learning effectiveness; the child model might not fully comprehend the parent model’s prediction strategy, potentially affecting overall translation performance. To address this, we propose a training strategy called Token-Level Filter Training, designed to effectively filter out unstable token predictions from the parent model, thereby transferring the parent model’s positive knowledge to the child model. Additionally, we introduce a hierarchical ranking loss method to help the child model better learn the parent model’s prediction strategies and sequence order, thus enhancing translation accuracy and fluency. Experimental results show that our method outperforms baseline methods on the public datasets Global Voices (Id, Ca, Hu, Pl) and WMT17 (Turkish–English), with BLEU score improvements of 1.47, 0.91, 0.50, 0.54, and 0.55, respectively. These results demonstrate the effectiveness and superiority of the proposed method.