最近，引入了亚图增强图神经网络（SGNN），以增强图形神经网络（GNN）的表达能力，事实证明，该功能不高于一维Weisfeiler-Leman同构测试。新的范式建议使用从输入图中提取的子图提高模型的表现力，但是额外的复杂性加剧了GNNS中本来可以具有挑战性的问题：解释其预测。在这项工作中，我们将PGEXPlainer（GNNS的最新解释者之一）改编为SGNN。拟议的解释器解释了所有不同子图的贡献，并可以产生人类可以解释的有意义的解释。我们在真实和合成数据集上执行的实验表明，我们的框架成功地解释了SGNN在图形分类任务上的决策过程。

当神经网络失去先前从不同分布的样本（即新任务）培训一组样本时，发生灾难性遗忘（CF）。现有方法在减轻CF方面取得了显着的结果，尤其是在称为任务增量学习的情况下。但是，这种情况是不现实的，并且已经完成了有限的工作以在更现实的情况下取得良好的结果。在本文中，我们提出了一种称为Centroid匹配的新型正则化方法，该方法受到元学习方法的启发，通过在神经网络产生的功能空间中操作来打击CF，在需要较小的记忆足迹的同时，取得了良好的结果。具体而言，该方法使用神经网络产生的特征向量直接对样品进行了分类，通过将这些向量与代表当前任务中的类或所有任务的质心匹配，直到该点。质心匹配速度比竞争基线更快，并且可以通过在过去的任务结束时保留模型产生的嵌入式空间之间的距离，并且可以利用它有效地减轻CF，而当前生产的距离则可以实现高精度的方法在所有任务上，在轻松场景上操作时，或不使用外部内存，或者将小型内存用于更现实的记忆。广泛的实验表明，匹配的质心在多个数据集和方案上取得了准确的提高。

Catastrophic forgetting (CF) happens whenever a neural network overwrites past knowledge while being trained on new tasks. Common techniques to handle CF include regularization of the weights (using, e.g., their importance on past tasks), and rehearsal strategies, where the network is constantly re-trained on past data. Generative models have also been applied for the latter, in order to have endless sources of data. In this paper, we propose a novel method that combines the strengths of regularization and generative-based rehearsal approaches. Our generative model consists of a normalizing flow (NF), a probabilistic and invertible neural network, trained on the internal embeddings of the network. By keeping a single NF throughout the training process, we show that our memory overhead remains constant. In addition, exploiting the invertibility of the NF, we propose a simple approach to regularize the network's embeddings with respect to past tasks. We show that our method performs favorably with respect to state-of-the-art approaches in the literature, with bounded computational power and memory overheads.

In this paper, we investigate the degree of explainability of graph neural networks (GNNs). Existing explainers work by finding global/local subgraphs to explain a prediction, but they are applied after a GNN has already been trained. Here, we propose a meta-learning framework for improving the level of explainability of a GNN directly at training time, by steering the optimization procedure towards what we call `interpretable minima'. Our framework (called MATE, MetA-Train to Explain) jointly trains a model to solve the original task, e.g., node classification, and to provide easily processable outputs for downstream algorithms that explain the model's decisions in a human-friendly way. In particular, we meta-train the model's parameters to quickly minimize the error of an instance-level GNNExplainer trained on-the-fly on randomly sampled nodes. The final internal representation relies upon a set of features that can be `better' understood by an explanation algorithm, e.g., another instance of GNNExplainer. Our model-agnostic approach can improve the explanations produced for different GNN architectures and use any instance-based explainer to drive this process. Experiments on synthetic and real-world datasets for node and graph classification show that we can produce models that are consistently easier to explain by different algorithms. Furthermore, this increase in explainability comes at no cost for the accuracy of the model.

图表表示学习已经成为许多情景中的无处不在的组成部分，从社会网络分析到智能电网的能量预测。在几个应用程序中，确保关于某些受保护属性的节点（或图形）表示的公平对其正确部署至关重要。然而，图表深度学习的公平仍然在探索，很少有解决方案。特别地，在若干真实世界图（即同声源性）上相似节点对簇的趋势可以显着恶化这些程序的公平性。在本文中，我们提出了一种新颖的偏见边缘辍学算法（Fairdrop）来反击精神剧并改善图形表示学习中的公平性。 Fairdrop可以在许多现有算法上轻松插入，具有高效，适应性，并且可以与其他公平诱导的解决方案结合。在描述了一般算法之后，我们在两个基准任务中展示其应用，具体地，作为用于生产节点嵌入的随机步道模型，以及用于链路预测的图形卷积网络。我们证明，所提出的算法可以成功地改善所有型号的公平，直到精度小或可忽略的降低，并与现有的最先进的解决方案相比。在一个消融研究中，我们证明我们的算法可以灵活地在偏置公平性和无偏见的边缘辍学之间插入。此外，为了更好地评估增益，我们提出了一种新的二元组定义，以测量与基于组的公平度量配对时的链路预测任务的偏差。特别是，我们扩展了用于测量节点嵌入的偏差的指标，以考虑图形结构。

已知非线性模型可在经常在非理想条件下运行的现实世界应用中提供出色的性能。但是，这些应用程序通常需要使用有限的计算资源进行在线处理。为了解决这个问题，我们为在线应用程序提出了一类新的高效非线性模型。所提出的算法基于使用功能链路扩展的线性参数（LIP）非线性过滤器。为了使这类功能链路自适应过滤器（FLAFS）有效，我们建议参数的低复杂性扩展和频域自适应。在该算法家族中，我们还定义了分区的频率域FLAF，其实现特别适合在线非线性建模问题。我们评估和比较频域FLAF与不同的扩展，从而在性能和计算复杂性之间提供了最佳的权衡。实验结果证明，即使在存在不良的非线性条件和计算资源的可用性有限的情况下，也可以将所提出的算法视为用于在线应用的有效解决方案，例如声音回声取消。

Linguists distinguish between novel and conventional metaphor, a distinction which the metaphor detection task in NLP does not take into account. Instead, metaphoricity is formulated as a property of a token in a sentence, regardless of metaphor type. In this paper, we investigate the limitations of treating conventional metaphors in this way, and advocate for an alternative which we name 'metaphorical polysemy detection' (MPD). In MPD, only conventional metaphoricity is treated, and it is formulated as a property of word senses in a lexicon. We develop the first MPD model, which learns to identify conventional metaphors in the English WordNet. To train it, we present a novel training procedure that combines metaphor detection with word sense disambiguation (WSD). For evaluation, we manually annotate metaphor in two subsets of WordNet. Our model significantly outperforms a strong baseline based on a state-of-the-art metaphor detection model, attaining an ROC-AUC score of .78 (compared to .65) on one of the sets. Additionally, when paired with a WSD model, our approach outperforms a state-of-the-art metaphor detection model at identifying conventional metaphors in text (.659 F1 compared to .626).

A widely acknowledged shortcoming of WordNet is that it lacks a distinction between word meanings which are systematically related (polysemy), and those which are coincidental (homonymy). Several previous works have attempted to fill this gap, by inferring this information using computational methods. We revisit this task, and exploit recent advances in language modelling to synthesise homonymy annotation for Princeton WordNet. Previous approaches treat the problem using clustering methods; by contrast, our method works by linking WordNet to the Oxford English Dictionary, which contains the information we need. To perform this alignment, we pair definitions based on their proximity in an embedding space produced by a Transformer model. Despite the simplicity of this approach, our best model attains an F1 of .97 on an evaluation set that we annotate. The outcome of our work is a high-quality homonymy annotation layer for Princeton WordNet, which we release.

Binarized Neural Networks (BNNs) are receiving increasing attention due to their lightweight architecture and ability to run on low-power devices. The state-of-the-art for training classification BNNs restricted to few-shot learning is based on a Mixed Integer Programming (MIP) approach. This paper proposes the BeMi ensemble, a structured architecture of BNNs based on training a single BNN for each possible pair of classes and applying a majority voting scheme to predict the final output. The training of a single BNN discriminating between two classes is achieved by a MIP model that optimizes a lexicographic multi-objective function according to robustness and simplicity principles. This approach results in training networks whose output is not affected by small perturbations on the input and whose number of active weights is as small as possible, while good accuracy is preserved. We computationally validate our model using the MNIST and Fashion-MNIST datasets using up to 40 training images per class. Our structured ensemble outperforms both BNNs trained by stochastic gradient descent and state-of-the-art MIP-based approaches. While the previous approaches achieve an average accuracy of 51.1% on the MNIST dataset, the BeMi ensemble achieves an average accuracy of 61.7% when trained with 10 images per class and 76.4% when trained with 40 images per class.

One of the common traits of past and present approaches for Semantic Role Labeling (SRL) is that they rely upon discrete labels drawn from a predefined linguistic inventory to classify predicate senses and their arguments. However, we argue this need not be the case. In this paper, we present an approach that leverages Definition Modeling to introduce a generalized formulation of SRL as the task of describing predicate-argument structures using natural language definitions instead of discrete labels. Our novel formulation takes a first step towards placing interpretability and flexibility foremost, and yet our experiments and analyses on PropBank-style and FrameNet-style, dependency-based and span-based SRL also demonstrate that a flexible model with an interpretable output does not necessarily come at the expense of performance. We release our software for research purposes at https://github.com/SapienzaNLP/dsrl.