Relation classification, a crucial component of relation extraction, involves
identifying connections between two entities. Previous studies have
predominantly focused on integrating the attention mechanism into relation
classification at a global scale, overlooking the importance of the local
context. To address this gap, this paper introduces a novel global-local
attention mechanism for relation classification, which enhances global
attention with a localized focus. Additionally, we propose innovative hard and
soft localization mechanisms to identify potential keywords for local
attention. By incorporating both hard and soft localization strategies, our
approach offers a more nuanced and comprehensive understanding of the
contextual cues that contribute to effective relation classification. Our
experimental results on the SemEval-2010 Task 8 dataset highlight the superior
performance of our method compared to previous attention-based approaches in
relation classification.

本文介绍了一种新颖的全局 - 局部注意机制用于关系分类，通过将全局注意力与局部关注度相结合来提升全局关注度。此外，我们提出了创新的硬与软定位机制，以识别局部注意的潜在关键字。通过同时融入硬和软定位策略，我们的方法对有效关系分类的语境线索提供了更加细致全面的理解。我们在 SemEval-2010 任务 8 数据集上的实验结果显示了我们的方法相对于前期关注机制的优越性能。

全局 - 局部关注机制的关系分类

A Global-Local Attention Mechanism for Relation Classification

Text-video retrieval is a critical multi-modal task to find the most relevant
video for a text query. Although pretrained models like CLIP have demonstrated
impressive potential in this area, the rising cost of fully finetuning these
models due to increasing model size continues to pose a problem. To address
this challenge, prompt tuning has emerged as an alternative. However, existing
works still face two problems when adapting pretrained image-text models to
downstream video-text tasks: (1) The visual encoder could only encode
frame-level features and failed to extract global-level general video
information. (2) Equipping the visual and text encoder with separated prompts
failed to mitigate the visual-text modality gap. To this end, we propose DGL, a
cross-modal Dynamic prompt tuning method with Global-Local video attention. In
contrast to previous prompt tuning methods, we employ the shared latent space
to generate local-level text and frame prompts that encourage inter-modal
interaction. Furthermore, we propose modeling video in a global-local attention
mechanism to capture global video information from the perspective of prompt
tuning. Extensive experiments reveal that when only 0.67% parameters are tuned,
our cross-modal prompt tuning strategy DGL outperforms or is comparable to
fully finetuning methods on MSR-VTT, VATEX, LSMDC, and ActivityNet datasets.
Code will be available at this https URL

我们提出了一个跨模态的动态提示调整方法，使用全局 - 局部注意机制对视频进行建模，通过仅调整 0.67％的参数，我们的方法在 MSR-VTT，VATEX，LSMDC 和 ActivityNet 数据集上优于或与完全微调方法相当。

DGL: 文本 - 视频检索的动态全局 - 局部提示调优

DGL: Dynamic Global-Local Prompt Tuning for Text-Video Retrieval

Transformer models have advanced the state of the art in many Natural
Language Processing (NLP) tasks. In this paper, we present a new Transformer
architecture, Extended Transformer Construction (ETC), that addresses two key
challenges of standard Transformer architectures, namely scaling input length
and encoding structured inputs. To scale attention to longer inputs, we
introduce a novel global-local attention mechanism between global tokens and
regular input tokens. We also show that combining global-local attention with
relative position encodings and a Contrastive Predictive Coding (CPC)
pre-training objective allows ETC to encode structured inputs. We achieve
state-of-the-art results on four natural language datasets requiring long
and/or structured inputs.

本文介绍了一种新的 Transformer 架构 ——Extended Transformer Construction（ETC），该架构解决了标准 Transformer 架构的两个关键挑战，即扩展输入长度和编码结构化输入，并且通过将全局 - 局部注意力机制与相对位置编码以及对比预测编码（CPC）预训练目标结合使用，实现了对结构化输入的编码和处理，在需要长或结构化输入的四个自然语言数据集上取得了领先的成果。