Research in scene graph generation (SGG) usually considers two-stage models, that is, detecting a set of entities, followed by combining them and labelling all possible relationships.

We investigate the entity alignment problem with unlabeled dangling cases, meaning that there are entities in the source or target graph having no counterparts in the other, and those entities remain unlabeled. The problem arises when the source and target graphs are of different scales, and it is much cheaper to label the matchable pairs than the dangling entities. To solve the issue, we propose a novel GNN-based dangling detection and entity alignment framework. While the two tasks share the same GNN and are trained together, the detected dangling entities are removed in the alignment. Our framework is featured by a designed entity and relation attention mechanism for selective neighborhood aggregation in representation learning, as well as a positive-unlabeled learning loss for an unbiased estimation of dangling entities. Experimental results have shown that each component of our design contributes to the overall alignment performance which is comparable or superior to baselines, even if the baselines additionally have 30\% of the dangling entities labeled as training data.

Information in industry, research, and the public sector is widely stored as rendered documents (e.g., PDF files, scans). Hence, to enable downstream tasks, systems are needed that map rendered documents onto a structured hierarchical format. However, existing systems for this task are limited by heuristics and are not end-to-end trainable. In this work, we introduce the Document Structure Generator (DSG), a novel system for document parsing that is fully end-to-end trainable. DSG combines a deep neural network for parsing (i) entities in documents (e.g., figures, text blocks, headers, etc.) and (ii) relations that capture the sequence and nested structure between entities. Unlike existing systems that rely on heuristics, our DSG is trained end-to-end, making it effective and flexible for real-world applications. We further contribute a new, large-scale dataset called E-Periodica comprising real-world magazines with complex document structures for evaluation. Our results demonstrate that our DSG outperforms commercial OCR tools and, on top of that, achieves state-of-the-art performance. To the best of our knowledge, our DSG system is the first end-to-end trainable system for hierarchical document parsing.

Few-shot named entity recognition (NER) systems aims at recognizing new classes of entities based on a few labeled samples. A significant challenge in the few-shot regime is prone to overfitting than the tasks with abundant samples. The heavy overfitting in few-shot learning is mainly led by spurious correlation caused by the few samples selection bias. To alleviate the problem of the spurious correlation in the few-shot NER, in this paper, we propose a causal intervention-based few-shot NER method. Based on the prototypical network, the method intervenes in the context and prototype via backdoor adjustment during training. In particular, intervening in the context of the one-shot scenario is very difficult, so we intervene in the prototype via incremental learning, which can also avoid catastrophic forgetting. Our experiments on different benchmarks show that our approach achieves new state-of-the-art results (achieving up to 29% absolute improvement and 12% on average for all tasks).

Does the name Audrey Hepburn ring a bell? Way back in 1952, it was the first computer speech recognition tool. Even though the technology was groundbreaking, the software could understand only digits. Since then, transcription software has come a long way. Today, transcription tools use immersive technologies like artificial intelligence (AI) to convert speech to text.

This paper studies a new problem setting of entity alignment for knowledge graphs (KGs). Since KGs possess different sets of entities, there could be entities that cannot find alignment across them, leading to the problem of dangling entities. As the first attempt to this problem, we construct a new dataset and design a multi-task learning framework for both entity alignment and dangling entity detection. The framework can opt to abstain from predicting alignment for the detected dangling entities. We propose three techniques for dangling entity detection that are based on the distribution of nearest-neighbor distances, i.e., nearest neighbor classification, marginal ranking and background ranking. After detecting and removing dangling entities, an incorporated entity alignment model in our framework can provide more robust alignment for remaining entities. Comprehensive experiments and analyses demonstrate the effectiveness of our framework. We further discover that the dangling entity detection module can, in turn, improve alignment learning and the final performance. The contributed resource is publicly available to foster further research.

PDFs, scans) into hierarchical structures is extensively demanded in the daily routines of many real-world applications, and is often a prerequisite step of many downstream NLP tasks. Earlier attempts focused on different but simpler tasks such as the detection of table or cell locations within documents; however, a holistic, principled approach to inferring the complete hierarchical structure in documents is missing. As a remedy, we developed "Doc-Parser": an end-to-end system for parsing the complete document structure - including all text elements, figures, tables, and table cell structures. To the best of our knowledge, Doc-Parser is the first system that derives the full hierarchical document compositions. Given the complexity of the task, annotating appropriate datasets is costly. Therefore, our second contribution is to provide a dataset for evaluating hierarchical document structure parsing. Our third contribution is to propose a scalable learning framework for settings where domain-specific data is scarce, which we address by a novel approach to weak supervision. Our computational experiments confirm the effectiveness of our proposed weak supervision: Compared to the baseline without weak supervision, it improves the mean average precision for detecting document entities by 37.1 % . When classifying hierarchical relations between entity pairs, it improves the F1 score by 27.6 % . 1 Introduction