Multimodal Named Entity Recognition (MNER) models typically require a significant volume of labeled data for effective training to extract relations between entities. In real-world scenarios, we frequently encounter unseen relation types. Nevertheless, existing methods are predominantly tailored for complete datasets and are not equipped to handle these new relation types. In this paper, we introduce the Few-shot Multimodal Named Entity Recognition (FMNER) task to address these novel relation types. FMNER trains in the source domain (seen types) and tests in the target domain (unseen types) with different distributions. Due to limited available resources for sampling, each sampling instance yields different content, resulting in data bias and alignment problems of multimodal units (image patches and words). To alleviate the above challenge, we propose a novel Multimodal causal Intervention graphs (MOUSING) model for FMNER. Specifically, we begin by constructing a multimodal graph that incorporates fine-grained information from multiple modalities. Subsequently, we introduce the Multimodal Causal Intervention Strategy to update the multimodal graph. It aims to decrease spurious correlations and emphasize accurate correlations between multimodal units, resulting in effectively aligned multimodal representations. Extensive experiments on two multimodal named entity recognition datasets demonstrate the superior performance of our model in the few-shot setting.

Document-level relation extraction (DocRE) aims to infer complex semantic relations among entities in a document. Distant supervision (DS) is able to generate massive auto-labeled data, which can improve DocRE performance. Recent works leverage pseudo labels generated by the pre-denoising model to reduce noise in DS data. However, unreliable pseudo labels bring new noise, e.g., adding false pseudo labels and losing correct DS labels. Therefore, how to select effective pseudo labels to denoise DS data is still a challenge in document-level distant relation extraction. To tackle this issue, we introduce uncertainty estimation technology to determine whether pseudo labels can be trusted. In this work, we propose a Document-level distant Relation Extraction framework with Uncertainty Guided label denoising, UGDRE. Specifically, we propose a novel instance-level uncertainty estimation method, which measures the reliability of the pseudo labels with overlapping relations. By further considering the long-tail problem, we design dynamic uncertainty thresholds for different types of relations to filter high-uncertainty pseudo labels. We conduct experiments on two public datasets. Our framework outperforms strong baselines by 1.91 F1 and 2.28 Ign F1 on the RE-DocRED dataset.

We have witnessed the rapid proliferation of multimodal data on numerous social media platforms. Conventional studies typically require massive labeled data to train models for Multimodal Aspect-Based Sentiment Analysis (MABSA). However, collecting and annotating fine-grained multimodal data for MABSA is tough. To alleviate the above issue, we perform three MABSA-related tasks with quite a small number of labeled multimodal samples. We first build diverse and comprehensive multimodal few-shot datasets according to the data distribution. To capture the specific prompt for each aspect term in a few-shot scenario, we propose a novel Generative Multimodal Prompt (GMP) model for MABSA, which includes the Multimodal Encoder module and the N-Stream Decoders module. We further introduce a subtask to predict the number of aspect terms in each instance to construct the multimodal prompt. Extensive experiments on two datasets demonstrate that our approach outperforms strong baselines on two MABSA-related tasks in the few-shot setting.

With the popularity of smartphones, we have witnessed the rapid proliferation of multimodal posts on various social media platforms. We observe that the multimodal sentiment expression has specific global characteristics, such as the interdependencies of objects or scenes within the image. However, most previous studies only considered the representation of a single image-text post and failed to capture the global co-occurrence characteristics of the dataset. In this paper, we propose Multi-channel Graph Neural Networks with Sentiment-awareness (MGNNS) for image-text sentiment detection. Specifically, we first encode different modalities to capture hidden representations. Then, we introduce multi-channel graph neural networks to learn multimodal representations based on the global characteristics of the dataset. Finally, we implement multimodal in-depth fusion with the multi-head attention mechanism to predict the sentiment of image-text pairs. Extensive experiments conducted on three publicly available datasets demonstrate the effectiveness of our approach for multimodal sentiment detection.