Multi-modal entity alignment (MMEA) aims to identify equivalent entities between two multi-modal knowledge graphs for integration. Unfortunately, prior arts have attempted to improve the interaction and fusion of multi-modal information, which have overlooked the influence of modal-specific noise and the usage of labeled and unlabeled data in semi-supervised settings. In this work, we introduce a Pseudo-label Calibration Multi-modal Entity Alignment (PCMEA) in a semi-supervised way. Specifically, in order to generate holistic entity representations, we first devise various embedding modules and attention mechanisms to extract visual, structural, relational, and attribute features. Different from the prior direct fusion methods, we next propose to exploit mutual information maximization to filter the modal-specific noise and to augment modal-invariant commonality. Then, we combine pseudo-label calibration with momentum-based contrastive learning to make full use of the labeled and unlabeled data, which improves the quality of pseudo-label and pulls aligned entities closer. Finally, extensive experiments on two MMEA datasets demonstrate the effectiveness of our PCMEA, which yields state-of-the-art performance.

Knowledge Graph (KG) have attained notable triumph over Question Answering (QA) tasks. However, the presence of temporal constraints on numerous facts within the real world has sparked heightened interest towards Temporal KGQA (TKGQA). Although previous methods have achieved great progress, they still have the following limitations: 1)PLMs cannot capture the entity drift caused by time constraints in the question. 2) Complex questions require multi-hop reasoning between entities. 3) Fusion strategies (addition or concatenation) of PLMs and KG information ignore feature differences, resulting in suboptimal solutions. To alleviate the above problems, we propose a novel Multi-view, Multi-hop and Multi-stage reasoning paradigm for TKGQA (M3TQA). Specifically, we first design a multi-view calibration module for fusing KG information to calibrate question representation. We next construct graph neural network in a multi-hop modeling module to capture multi-hop message passing between entities. Finally, we design multi-stage aggregation that facilitates the adaptive fusion of heterogeneous information with a two-stage interaction alignment process. The performance on two mainstream benchmark datasets verifies the effectiveness of our proposed model.

The Chinese NER task consists of two steps, first determining entity boundaries and then labeling them. Some previous work incorporating related words from pre-trained vocabulary into character-based models has been demonstrated to be effective. However, the number of words that characters can match in the vocabulary is large, and their meanings vary widely. It is unreasonable to concatenate all the matched words into the character's representation without making semantic distinctions. This is because words with different semantics also have distinct vectors by the distributed representation. Moreover, mutual information maximization (MIM) provides a unified way to characterize the correction between different granularity of embeddings, we find it can be used to enhance the features in our task. Consequently, this paper introduces a novel Chinese NER model named SSMI based on semantic similarity and MIM. We first match all the potential word boundaries of the input characters from the pre-trained vocabulary and employ BERT to segment the input sentence to get the segmentation containing these characters. After computing their cosine similarity, we obtain the word boundary with the highest similarity and the word group with similarity score larger than a specific threshold. Then, we concatenate the most relevant word boundaries with character vectors. We further calculate the mutual information maximization of group, character and sentence, respectively. Finally, we feed the result from the above steps to our novel network. The results on four Chinese public NER datasets show that our SSMI achieves state-of-the-art performance.