Addressing the challenge of low-resource information extraction remains an
ongoing issue due to the inherent information scarcity within limited training
examples. Existing data augmentation methods, considered potential solutions,
struggle to strike a balance between weak augmentation (e.g., synonym
augmentation) and drastic augmentation (e.g., conditional generation without
proper guidance). This paper introduces a novel paradigm that employs targeted
augmentation and back validation to produce augmented examples with enhanced
diversity, polarity, accuracy, and coherence. Extensive experimental results
demonstrate the effectiveness of the proposed paradigm. Furthermore, identified
limitations are discussed, shedding light on areas for future improvement.

应对低资源信息提取的挑战仍然是一个持续存在的问题，由于有限训练示例中固有的信息稀缺性。现有的数据增强方法被认为是潜在的解决方案，但在弱增强（例如同义词增强）和剧烈增强（例如缺乏适当指导的条件生成）之间很难达到平衡。本文提出了一种新的范式，采用目标增强和反向验证，以生成增强的示例，增强了多样性、极性、准确性和连贯性。广泛的实验结果证明了所提范式的有效性。此外，还讨论了已确定的局限性，为未来改进提供了启示。

针对低资源事件抽取的目标增强

Targeted Augmentation for Low-Resource Event Extraction

Multi-objective combinatorial optimization (MOCO) problems are prevalent in
various real-world applications. Most existing neural methods for MOCO problems
rely solely on decomposition and utilize precise hypervolume to enhance
diversity. However, these methods often approximate only limited regions of the
Pareto front and spend excessive time on diversity enhancement because of
ambiguous decomposition and time-consuming hypervolume calculation. To address
these limitations, we design a Geometry-Aware Pareto set Learning algorithm
named GAPL, which provides a novel geometric perspective for neural MOCO via a
Pareto attention model based on hypervolume expectation maximization. In
addition, we propose a hypervolume residual update strategy to enable the
Pareto attention model to capture both local and non-local information of the
Pareto set/front. We also design a novel inference approach to further improve
quality of the solution set and speed up hypervolume calculation and local
subset selection. Experimental results on three classic MOCO problems
demonstrate that our GAPL outperforms state-of-the-art neural baselines via
superior decomposition and efficient diversity enhancement.

通过基于超体积期望最大化的 Pareto 注意模型以及超体积残差更新策略，结合新颖的推理方法和局部子集选择方法，设计了一种名为 GAPL 的几何感知 Pareto 集学习算法，用于解决多目标组合优化问题，提高问题的分解能力和多样性增强。通过三个经典的多目标组合优化问题的实验结果表明，GAPL 算法在分解和多样性增强方面优于现有的神经基准模型。

面向几何感知的神经多目标组合优化 Pareto 集学习

Towards Geometry-Aware Pareto Set Learning for Neural Multi-Objective  Combinatorial Optimization

Scene graph generation (SGG) aims to detect objects and predict the
relationships between each pair of objects. Existing SGG methods usually suffer
from several issues, including 1) ambiguous object representations, as graph
neural network-based message passing (GMP) modules are typically sensitive to
spurious inter-node correlations, and 2) low diversity in relationship
predictions due to severe class imbalance and a large number of missing
annotations. To address both problems, in this paper, we propose a regularized
unrolling network (RU-Net). We first study the relation between GMP and graph
Laplacian denoising (GLD) from the perspective of the unrolling technique,
determining that GMP can be formulated as a solver for GLD. Based on this
observation, we propose an unrolled message passing module and introduce an
$\ell_p$-based graph regularization to suppress spurious connections between
nodes. Second, we propose a group diversity enhancement module that promotes
the prediction diversity of relationships via rank maximization. Systematic
experiments demonstrate that RU-Net is effective under a variety of settings
and metrics. Furthermore, RU-Net achieves new state-of-the-arts on three
popular databases: VG, VRD, and OI. Code is available at
this https URL

本文提出了一种规范化展开网络 RU-Net 来改善场景图生成中的物体表示模糊和关系预测低多样性等问题，其中包括一种非常基于 unrolling message passing modules 和一种推动关系多样性的 group diversity enhancement module，实验结果表明该方法在多种设置和指标下都同样有效，并在三个流行数据库上取得了新的最先进成果。