Chinese geographic re-ranking task aims to find the most relevant addresses among retrieved candidates, which is crucial for location-related services such as navigation maps. Unlike the general sentences, geographic contexts are closely intertwined with geographical concepts, from general spans (e.g., province) to specific spans (e.g., road). Given this feature, we propose an innovative framework, namely Geo-Encoder, to more effectively integrate Chinese geographical semantics into re-ranking pipelines. Our methodology begins by employing off-the-shelf tools to associate text with geographical spans, treating them as chunking units. Then, we present a multi-task learning module to simultaneously acquire an effective attention matrix that determines chunk contributions to extra semantic representations. Furthermore, we put forth an asynchronous update mechanism for the proposed addition task, aiming to guide the model capable of effectively focusing on specific chunks. Experiments on two distinct Chinese geographic re-ranking datasets, show that the Geo-Encoder achieves significant improvements when compared to state-of-the-art baselines. Notably, it leads to a substantial improvement in the Hit@1 score of MGEO-BERT, increasing it by 6.22% from 62.76 to 68.98 on the GeoTES dataset.

翻译：中文地理重排序任务旨在从检索候选中找出最相关地址，这对导航地图等位置相关服务至关重要。与通用句子不同，地理上下文与地理概念紧密交织，涵盖从宽泛范围（如省份）到具体范围（如道路）的跨度。针对这一特性，本文提出创新框架Geo-Encoder，以更有效地将中文地理语义融入重排序流程。该方法首先利用现成工具将文本与地理跨度关联，将其视为分块单元；随后提出多任务学习模块，同步获取确定文本块对额外语义表征贡献度的有效注意力矩阵；此外，针对所提出的新增任务设计了异步更新机制，引导模型能够有效聚焦特定文本块。在两个不同中文地理重排序数据集上的实验表明，与最先进基线相比，Geo-Encoder取得了显著提升。值得注意的是，在GeoTES数据集上，该方法使MGEO-BERT的Hit@1得分从62.76%提升至68.98%，增幅达6.22%。