We present ProtoTEx, a novel white-box NLP classification architecture based
on prototype networks. ProtoTEx faithfully explains model decisions based on
prototype tensors that encode latent clusters of training examples. At
inference time, classification decisions are based on the distances between the
input text and the prototype tensors, explained via the training examples most
similar to the most influential prototypes. We also describe a novel
interleaved training algorithm that effectively handles classes characterized
by the absence of indicative features. On a propaganda detection task, ProtoTEx
accuracy matches BART-large and exceeds BERT-large with the added benefit of
providing faithful explanations. A user study also shows that prototype-based
explanations help non-experts to better recognize propaganda in online news.

ProtoTEx 是一种基于原型网络的新型白盒 NLP 分类体系结构，其通过原型张量解释模型决策，可在处理没有代表性特征的类别时有效地处理，可精准检测网络新闻中的宣传，并通过原型解释帮助非专业人士更好地识别宣传。

ProtoTEx: 用原型张量解释模型决策

ProtoTEx: Explaining Model Decisions with Prototype Tensors

Despite significant advances in graph representation learning, little
attention has been paid to the more practical continual learning scenario in
which new categories of nodes (e.g., new research areas in citation networks,
or new types of products in co-purchasing networks) and their associated edges
are continuously emerging, causing catastrophic forgetting on previous
categories. Existing methods either ignore the rich topological information or
sacrifice plasticity for stability. To this end, we present Hierarchical
Prototype Networks (HPNs) which extract different levels of abstract knowledge
in the form of prototypes to represent the continuously expanded graphs.
Specifically, we first leverage a set of Atomic Feature Extractors (AFEs) to
encode both the elemental attribute information and the topological structure
of the target node. Next, we develop HPNs to adaptively select relevant AFEs
and represent each node with three levels of prototypes. In this way, whenever
a new category of nodes is given, only the relevant AFEs and prototypes at each
level will be activated and refined, while others remain uninterrupted to
maintain the performance over existing nodes. Theoretically, we first
demonstrate that the memory consumption of HPNs is bounded regardless of how
many tasks are encountered. Then, we prove that under mild constraints,
learning new tasks will not alter the prototypes matched to previous data,
thereby eliminating the forgetting problem. The theoretical results are
supported by experiments on five datasets, showing that HPNs not only
outperform state-of-the-art baseline techniques but also consume relatively
less memory.

本文提出一种基于原子特征提取器和分层原型网络的深度学习模型来解决连续扩展的图数据中面临的遗忘问题，并对其理论进行了证明，并在五个数据集上进行了实验验证。