Multilingual transformer-based models demonstrate remarkable zero and
few-shot transfer across languages by learning and reusing language-agnostic
features. However, as a fixed-size model acquires more languages, its
performance across all languages degrades, a phenomenon termed interference.
Often attributed to limited model capacity, interference is commonly addressed
by adding additional parameters despite evidence that transformer-based models
are overparameterized. In this work, we show that it is possible to reduce
interference by instead identifying and pruning language-specific parameters.
First, we use Shapley Values, a credit allocation metric from coalitional game
theory, to identify attention heads that introduce interference. Then, we show
that removing identified attention heads from a fixed model improves
performance for a target language on both sentence classification and
structural prediction, seeing gains as large as 24.7\%. Finally, we provide
insights on language-agnostic and language-specific attention heads using
attention visualization.

通过识别和裁剪语言特定的参数，可以减少干扰，从而提高多语言变压器模型的性能，使用 Shapley Values 等指标来指导删除头注意力。

Shapley Head Pruning: 多语言变换器中干扰的识别与消除

Shapley Head Pruning: Identifying and Removing Interference in Multilingual Transformers

Retrieval networks are essential for searching and indexing. Compared to
classification networks, attention visualization for retrieval networks is
hardly studied. We formulate attention visualization as a constrained
optimization problem. We leverage the unit L2-Norm constraint as an attention
filter (L2-CAF) to localize attention in both classification and retrieval
networks. Unlike recent literature, our approach requires neither architectural
changes nor fine-tuning. Thus, a pre-trained network's performance is never
undermined
L2-CAF is quantitatively evaluated using weakly supervised object
localization. State-of-the-art results are achieved on classification networks.
For retrieval networks, significant improvement margins are achieved over a
Grad-CAM baseline. Qualitative evaluation demonstrates how the L2-CAF
visualizes attention per frame for a recurrent retrieval network. Further
ablation studies highlight the computational cost of our approach and compare
L2-CAF with other feasible alternatives. Code available at
this https URL

本研究提出了一种不需要结构性改变或微调卷积神经网络的方法，利用 L2-CAF 作为一种注意力滤波器，从而实现检索网络的注意力可视化，实现了在物体定位方面的最先进研究成果。

卷积神经网络的通用可视化方法

A Generic Visualization Approach for Convolutional Neural Networks

Deep models that are both effective and explainable are desirable in many
settings; prior explainable models have been unimodal, offering either
image-based visualization of attention weights or text-based generation of
post-hoc justifications. We propose a multimodal approach to explanation, and
argue that the two modalities provide complementary explanatory strengths. We
collect two new datasets to define and evaluate this task, and propose a novel
model which can provide joint textual rationale generation and attention
visualization. Our datasets define visual and textual justifications of a
classification decision for activity recognition tasks (ACT-X) and for visual
question answering tasks (VQA-X). We quantitatively show that training with the
textual explanations not only yields better textual justification models, but
also better localizes the evidence that supports the decision. We also
qualitatively show cases where visual explanation is more insightful than
textual explanation, and vice versa, supporting our thesis that multimodal
explanation models offer significant benefits over unimodal approaches.

本研究提出了一种多模态方法来解释深层模型，同时训练文本生成和注意权重可视化，研究结果表明该方法可以产生更好的文本解释模型，并能更好地定位支持分类决策的证据，从而明显优于单模态方法。