Out-of-context (OOC) misinformation often arises when an authentic news image is paired with misleading text, making the cross-modal evidence used by a detector an important object of interpretation. We present SparseAlign-OOC, a two-level sparse autoencoder (SAE) framework for auditing NewsCLIPpings CLIP-embedding detectors through recurring image-text alignment features. A pair-space SAE maps detector inputs into sparse coordinates whose high-activation examples form human-auditable concept groups, while a hidden-state SAE traces sparse features associated with detector decisions. In a 250-item blind human audit with three annotators, selected SAE groups yield 18/50 coherent-alignment concepts, compared with 9/50 for prototype clustering, 7/50 for PCA, 4/50 for a dense autoencoder, and 0/50 for random groups. A 150-item replication audit on a stronger deep MLP preserves the same ordering, producing pooled results of 29/80 SAE alignment groups versus 15/80 for the strongest baseline. Coverage analysis shows that the presence of concepts increases with activation strength. In contrast, direct pair-space interventions show that human-labeled alignment features produce larger detector changes than topical, incoherent, or random SAE features. In the NewsCLIPpings CLIP-embedding setting, SparseAlign-OOC provides a validated audit pipeline for exposing recurring alignment factors that are both human-auditable and functionally connected to detector behavior.

Multimodal pre-training with text, layout, and image has achieved SOTA performance for visually rich document understanding tasks recently, which demonstrates the great potential for joint learning across different modalities. However, the existed research work has focused only on the English domain while neglecting the importance of multilingual generalization. In this paper, we introduce a human-annotated multilingual form understanding benchmark dataset named XFUND, which includes form understanding samples in 7 languages (Chinese, Japanese, Spanish, French, Italian, German, Portuguese). Meanwhile, we present LayoutXLM, a multimodal pre-trained model for multilingual document understanding, which aims to bridge the language barriers for visually rich document understanding. Experimental results show that the LayoutXLM model has significantly outperformed the existing SOTA cross-lingual pre-trained models on the XFUND dataset. The XFUND dataset and the pre-trained LayoutXLM model have been publicly available at https://aka.ms/layoutxlm.

Positional encoding plays a key role in Transformer-based architecture, which is to indicate and embed token sequential order information. Understanding documents with unreliable reading order information is a real challenge for document Transformer models. This paper proposes a simple and effective positional encoding method, learnable sinusoidal positional encoding (LSPE), by building a learnable sinusoidal positional encoding feed-forward network. We apply LSPE to document Transformer models and pretrain them on document datasets. Then we finetune and evaluate the model performance on document understanding tasks in form, receipt, and invoice domains. Experimental results show our proposed method not only outperforms other baselines, but also demonstrates its robustness and stability on handling noisy data with incorrect order information.

Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born documents. We propose LayoutLMv2 architecture with new pre-training tasks to model the interaction among text, layout, and image in a single multi-modal framework. Specifically, with a two-stream multi-modal Transformer encoder, LayoutLMv2 uses not only the existing masked visual-language modeling task but also the new text-image alignment and text-image matching tasks, which make it better capture the cross-modality interaction in the pre-training stage. Meanwhile, it also integrates a spatial-aware self-attention mechanism into the Transformer architecture so that the model can fully understand the relative positional relationship among different text blocks. Experiment results show that LayoutLMv2 outperforms LayoutLM by a large margin and achieves new state-of-the-art results on a wide variety of downstream visually-rich document understanding tasks, including FUNSD (0.7895 to 0.8420), CORD (0.9493 to 0.9601), SROIE (0.9524 to 0.9781), Kleister-NDA (0.8340 to 0.8520), RVL-CDIP (0.9443 to 0.9564), and DocVQA (0.7295 to 0.8672).

Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters. Therefore, Chinese Word Segmentation (CWS) is usually considered as the first step for Chinese NER. However, models based on word-level embeddings and lexicon features often suffer from segmentation errors and out-of-vocabulary (OOV) words. In this paper, we investigate a Convolutional Attention Network called CAN for Chinese NER, which consists of a character-based convolutional neural network (CNN) with local-attention layer and a gated recurrent unit (GRU) with global self-attention layer to capture the information from adjacent characters and sentence contexts. Also, compared to other models, not depending on any external resources like lexicons and employing small size of char embeddings make our model more practical. Extensive experimental results show that our approach outperforms state-of-the-art methods without word embedding and external lexicon resources on different domain datasets including Weibo, MSRA and Chinese Resume NER dataset.